Post on 25-Mar-2018
iCIRRUS Contract No. 644526 1 Jan 2015 – 31 Dec 2017
This project has received funding from the European Union’s Horizon 2020
research and innovation programme under grant agreement No 644526
intelligent Converged network consolIdating Radio and optical access aRound
USer equipment
DELIVERABLE: D3.4
Updated Low-Cost, Energy-Efficient Fronthaul Architecture
Contract number: 644526
Project acronym: iCIRRUS
Project title: Intelligent converged network consolidating radio and optical access
around user equipment
Project duration: 1 January 2015 – 31 December 2017
Coordinator: Nathan Gomes, University of Kent, Canterbury, UK
Deliverable Number: D3.4
Type: Report
Dissemination level Public
Date submitted: 07.07.2017
Editors: Kai Habel (HHI)
Authors / contributors
(contributing partners)
HHI: Kai Habel, Christoph Kottke, Malte Hinrichs, Luz Fernandez del
Rosal
ADVA: Daniel Münch, Nicklas Eiselt
Kent: Philippos Assimakopoulos, Nathan Gomes
VIAVI: Howard Thomas
UEssex: Mike Parker, Felix Ngobigha, Geza Koczian, Terry Quinlan,
Stuart Walker
Orange: Philippe Chanclou
Internal reviewers Mike Parker, Philippe Chanclou
Contract No: 644526 iCIRRUS 1 Jan 2015 – 31 Dec 2017
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This project has received funding from the European
Union’s Horizon 2020 research and innovation
programme under grant agreement No 644526
Document history
0.0 Document creation 14/12/2016
0.1 First input HHI 08/05/2017
0.2 First Input UEssex, VIAVI 10/05/2017
0.3 Update VIAVI 15/05/2017
0.4 Merge of input from Kent,
Viavi and ADVA
29/05/2017
0.5 Update HHI 30/05/2017
0.6 Input from ADVA 31/05/2017
0.7 Input from UEssex integrated 01/06/2017
0.8 Input from Orange 01/06/2017
0.9 Update from UEssex
integrated
06/06/2017
0.95 Final touch for
Introduction/Conclusion
15/06/2017
0.96 Input from UKent 16/06/2017
0.97 Internal Reviews (Orange,
UEssex)
29/06/2017
0.99 Resolving review comments 05/07/2017
Final Final clarifications applied
(ADVA)
07/07/2017
Contract No: 644526 iCIRRUS 1 Jan 2015 – 31 Dec 2017
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This project has received funding from the European
Union’s Horizon 2020 research and innovation
programme under grant agreement No 644526
Abstract
This deliverable “Updated Low-Cost, Energy-Efficient Fronthaul Architecture” gives, as the title
suggests, an update of the 5G fronthaul architecture for iCIRRUS. The fronthaul requirements and
key performance indicators (KPIs), such as data rates or parameters for synchronisation, timing
accuracy, latency, and bit error rate have been again evaluated and updated in this document. The
key building blocks for the iCIRRUS solutions have been investigated and experimentally verified. The
main results are given.
Contract No: 644526 iCIRRUS 1 Jan 2015 – 31 Dec 2017
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This project has received funding from the European
Union’s Horizon 2020 research and innovation
programme under grant agreement No 644526
Executive Summary
This deliverable “Updated Low-Cost, Energy-Efficient Fronthaul Architecture” gives an update of the
fronthaul architecture as compared to the previous iCIRRUS deliverable D3.2, in terms of the 5G
requirements, KPIs, and technical building blocks. The iCIRRUS fronthaul comprises data streams
with different performance requirements, including synchronization, legacy fronthaul, evolved
fronthaul user data, fronthaul control data, and potentially backhaul traffic thanks to the structural
convergence enabled by Ethernet. The iCIRRUS architecture combines Ethernet as the transport
protocol with modifications to the functional split in order to reduce data rates in the fronthaul
while making statistical multiplexing gains possible, and thus allows a more efficient use of the
network resources. Extensive investigations for the various technical building blocks of the proposed
solutions are given.
Our analysis indicates that most KPIs are feasible within the iCIRRUS architecture and that the
challenges will reside in meeting timing and synchronization requirements. More advanced 5G
demonstration scenarios are planned for the final test in the project to confirm that the proposed
architecture meets the demanding fronthaul requirements that are expected from 5G networking.
Contract No: 644526 iCIRRUS 1 Jan 2015 – 31 Dec 2017
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This project has received funding from the European
Union’s Horizon 2020 research and innovation
programme under grant agreement No 644526
Index of terms
ADC Analogue to Digital Converter
API Application Programming Interface
APTS Assisted Partial Timing Support
ATM Asynchronous Transfer Mode
AWG Arbitrary Waveform Generator
BBU Baseband Unit
BC Boundary Clock
BER Bit Error Rate
BES Best Effort Section
BESS Best Effort Sub-Section
BF Basic Frame
BL Bit Loading
BSC Base Station Controller
btb Back to Back
BTS Base Station
BW Bandwidth
CD Chromatic Dispersion
CDR Clock Data Recovery
CFO Carrier Frequency Offset
CPRI Common Public Radio Interface
C&M Control and Management
CRC Cyclic Redundancy Check
C&M Control and Management
C-RAN Cloud Radio Access Network
CSI Channel State Information
CP Cyclic Prefix
CCDF Complementary cumulative distribution function
CO Central Office
CoMP Coordinated MultiPoint
CPRI Common Public Radio Interface
CQF Cyclic Queuing and Forwarding
D2D Device-to-Device
D2I Device-to-Infrastructure
DAC Digital to Analogue Converter
DCI Downlink Control Information
DD Direct Detection
DFB Distributed Feedback Laser
DL Downlink
DLSCH Downlink Shared Channel
DMRS Demodulation Reference Signal
DMT Discrete Multi-tone
DQPSK Differential Quadrature Phase-Shift Keying
DSO Digital Storage Oscilloscope
DSP Digital Signal Processing
DU Digital Unit
EDFA Erbium Doped Fibre Amplifier
EML Electro-Absorption Modulated Laser
Contract No: 644526 iCIRRUS 1 Jan 2015 – 31 Dec 2017
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This project has received funding from the European
Union’s Horizon 2020 research and innovation
programme under grant agreement No 644526
eNodeB (eNB) Evolved Node B
EPC Evolved Packet Core
EVM Error Vector Magnitude
FCS Frame Check Sequence
FDV Frame-delay variation
FEC Forward Error Correction
FFE Feed Forward Equalizer
FFT Fast Fourier Transform
FIFO First In, First-Out
FIL Fronthaul Interface Library
FIR Finite Impulse Response
FLR Frame Loss Rate
FPGA Field Programmable Gate Array
FQTSS Forwarding and Queuing Enhancements for Time-Sensitive Streams
FRP Frame Result Packet
FSPL Free Space Path Loss
FTTx Fibre To The x
FUSION
FWHM Full-Width Half-Maximum
3GPP 3rd Generation Partnership Project
GNSS Global Navigation Satellite System
GP Guard Period
GPS Global Positioning System
G-PON Gigabit Passive Optical Network
GST Guaranteed Service Transport
HARQ Hybrid Automatic Repeat Request
HD Hard Decision
HetNet Heterogeneous Network
HP High Priority
ID Identifier
IDFT Inverse Discrete Fourier Transform
IFFT Inverse Fast Fourier Transform
IM Intensity Modulation
IMDD Intensity Modulation and Direct Detection
IF Intermediate Frequency
IFFT Inverse Fast Fourier Transform
IMT International Mobile Telecommunications
IPU Intelligent Processing Unit
ITU-T International Telecommunication Union-Telecommunication Standardization Sector
iRRH Intelligent Remote Radio Head
IQ In-phase / Quadrature
IP Internet Protocol
JT Joint Transmission
KPI Key Performance Indicator
LC Lucent Connector
LTE Long Term Evolution
LAN Local Area Network
LO Local Oscillator
LP Low Priority
MAC Media Access Control
Contract No: 644526 iCIRRUS 1 Jan 2015 – 31 Dec 2017
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This project has received funding from the European
Union’s Horizon 2020 research and innovation
programme under grant agreement No 644526
MAN Metro Area Network
MB Mobile Cloud
MC Multi Carrier
MCS Modulation and Coding index
MEF Metro Ethernet Forum
MIMO Multiple-Input Multiple-Output
MME Mobility Management Entity
MRP Metric Result Packet
MTU Maximum Transfer Unit
MZM Mach-Zehnder Modulator
NFV Network Function Virtualization
NGFI Next-Generation Fronthaul Interface
NGMN Next Generation Mobile Network
NRZ Non-Return to Zero
OAI Open Air Interface
OAM Operations, Administration and Management
OBSAI Open Base Station Architecture Initiative
ODN Optical Distribution Network
OFDM Orthogonal Frequency Division Multiplexing
OLT Optical Line Termination
OMC Operations and Maintenance Center
ONU Optical Network Unit
OOK On-Off Keying
OSS Operation Support System
OTA Over-The-Air
OTG OAI Traffic Generator
OTT Over The Top
PAM Pulse Amplitude Modulation
PAM-4 Four-level Pulse Amplitude Modulation
PAPR Peak-to-Average Power Ratio
PDCP Packet Data Convergence Protocol
PDU Protocol Data Unit
PDSCH Physical Downlink Shared Channel
PDV Packet Delay Variation
PHICH Physical Hybrid-ARQ Indicator Channel
PHY Physical (Layer)
PMA Physical Medium Attachment Sublayer
PMD Physical Medium Dependent Sublayer
PMI Precoding Matrix Indicator
PL Power Loading
PLL Phase-Locked Loop
PON Passive Optical Network
POX Python-based 1 network operation system
PRACH Physical Random Access Channel
PRC Primary Clock
PRE Packet Routing Engine
1 http://searchsdn.techtarget.com/definition/POX
Contract No: 644526 iCIRRUS 1 Jan 2015 – 31 Dec 2017
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This project has received funding from the European
Union’s Horizon 2020 research and innovation
programme under grant agreement No 644526
PS Protected Section
PSD Power Spectrum Density
PSFP Per-Stream Filtering and Policing
PSS Protected Sub-Section
PtMP Point-to-Multi-Point
PtP Point-To-Point
PTP Precision Time Protocol
PUCCH Physical Uplink Control Channel
PUSCH Physical Downlink Shared Channel
QAM Quadrature Amplitude Modulation
QoE Quality of Experience
QoS Quality of Service
RAN Radio Access Network
RAR Random Access Response
RAT Radio Access Technology
RAU Remote Aggregator Unit
RE Radio Equipment
REC Remote Equipment Controller
RF Radio Frequency
RLC Radio Link Control
RNC Radio Network Controller
RoE Radio over Ethernet
ROP Received Optical Power
RRC Root Raised Cosine
RRH Remote Radio Head
RRS Radio Remote System
RRU Remote Radio Unit
RSTD Reference Signal Time Difference
RTT Round Trip Time
RU Remote Unit
Rx Receiver
SC Single Carrier
SD Soft Decision
SDH Synchronous Digital Hierarchy
SDN Software Defined Network
SDQPSK Single DPQSK
SFO Sampling Frequency Offset
SFP Small Form-factor Pluggable
SI System Information
SISO Single Input, single Output
SLA Service Level Agreement
SM Statistically Multiplexed
SN Sequence Number
SNR Signal-to-Noise Ratio
SON Self Optimising Network
SONET Synchronous Optical Networking
SP Strict Priority
SRAM Static Random-Access Memory
SRS Sounding Reference Signal
SSB Single Side Band
Contract No: 644526 iCIRRUS 1 Jan 2015 – 31 Dec 2017
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This project has received funding from the European
Union’s Horizon 2020 research and innovation
programme under grant agreement No 644526
SSMF Standard Single-Mode Fibre
SyncE Synchronous Ethernet
TAS Time-Aware Shaping
TB Transport Block
TDD Time Division Duplex
TDM Time Division Multiplexing
TDMA Time Division Multiplexing Access
TG Traffic Generator
TIA Trans-Impedance Amplifier
TS Training Symbol
TSN Time-Sensitive Networking
TTI Transmission Time Interval
TW Transmission Window
Tx Transmitter
UDP User Datagram Protocol
UE User Equipment
UL Uplink
USRP Universal Software Radio Peripheral
UTC Coordinated Universal Time
vBBU Virtual Baseband Unit
VLAN Virtual Local Area Network
VM Virtual Machine
VNA Vector Network Analyser
VOA Variable Optical Attenuator
WDM Wavelength Division Multiplexing
WFQ Weighted Fair Queuing
WRR Weight Round Robin
XGS-PON 10-Gigabit-capable Symmetric Passive Optical Network
Contract No: 644526 iCIRRUS 1 Jan 2015 – 31 Dec 2017
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This project has received funding from the European
Union’s Horizon 2020 research and innovation
programme under grant agreement No 644526
Contents 1 Introduction .................................................................................................................................. 13
2 Architecture overview ................................................................................................................... 13
2.1 Access Network Solutions for Ethernet Backhaul ................................................................. 14
2.2 Fixed Access Network segments for RAN fronthaul ............................................................. 15
2.3 New Fixed Networks segments for virtual RAN .................................................................... 17
2.4 Fronthaul deployment options ............................................................................................. 18
3 Refined fronthaul requirements and KPIs .................................................................................... 20
3.1 Data rates .............................................................................................................................. 20
3.1.1 Data rate for low layer RAN functional split ................................................................. 20
3.1.2 Data rate for High layer RAN functional split ................................................................ 21
3.2 Fronthaul Security ................................................................................................................. 23
3.2.1 Introduction to Security in 5G Access Networking ....................................................... 23
3.2.2 Security Considerations for 5G Architectures ............................................................... 24
3.3 Fronthaul KPIs ....................................................................................................................... 26
4 Updated architectural building blocks .......................................................................................... 26
4.1 Ethernet mapping and encapsulation ................................................................................... 26
4.2 Timing and synchronization (priority/scheduling) ................................................................ 33
4.3 Fronthaul intelligent processing unit (IPU) ........................................................................... 33
4.4 Time sensitive Ethernet switching /aggregation .................................................................. 36
4.4.1 Deterministic Ethernet transport with low and fixed latency ...................................... 36
4.4.2 Time-aware-shaping (TAS) reference scenario ............................................................. 43
4.5 High-speed and low-cost transmission links ......................................................................... 48
4.5.1 100 Gbit/s per wavelength ........................................................................................... 48
4.5.2 Beyond 100 Gbit/s per wavelength .............................................................................. 53
4.5.3 Wireless transmission over millimetre wave ................................................................ 57
4.6 Test and performance monitoring ........................................................................................ 62
5 Further architectural considerations ............................................................................................ 65
5.1 Evolved digital fronthaul vs. analogue fronthaul .................................................................. 65
5.1.1 Reference system .......................................................................................................... 65
5.1.2 Analogue Fronthaul ....................................................................................................... 65
5.1.3 Evolved Digital Fronthaul .............................................................................................. 66
5.1.4 Optical Link .................................................................................................................... 66
Contract No: 644526 iCIRRUS 1 Jan 2015 – 31 Dec 2017
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This project has received funding from the European
Union’s Horizon 2020 research and innovation
programme under grant agreement No 644526
5.1.5 Results ........................................................................................................................... 67
5.1.6 Comparison ................................................................................................................... 69
5.1.7 Conclusions ................................................................................................................... 69
5.2 SON use cases ....................................................................................................................... 69
5.2.1 Representation of the radio environment .................................................................... 70
5.2.2 Representation of the fronthaul environment ............................................................. 70
6 Conclusions ................................................................................................................................... 72
References ............................................................................................................................................ 73
List of figures ......................................................................................................................................... 76
List of tables .......................................................................................................................................... 78
Contract No: 644526 iCIRRUS 1 Jan 2015 – 31 Dec 2017
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This project has received funding from the European
Union’s Horizon 2020 research and innovation
programme under grant agreement No 644526
1 Introduction This deliverable D3.4 updates the architecture for the evolved fronthaul that has been
developed within the iCIRRUS project. It is mainly based on the technical solutions addressed in WP3
and enhanced by results of the other work packages (WPs). An architecture overview of 5G fronthaul
follows in section 2, also covering the most recent discussions of 5G deployment options. An update
relative to the previous deliverable D3.2 [51] for the requirements and KPIs for the mobile fronthaul
are covered in section 3. The technologies to realize the iCIRRUS fronthaul solutions are evaluated in
section 4. These are namely: Ethernet mapping and encapsulation, Timing and synchronization,
Fronthaul intelligent processing unit (IPU), Time sensitive Ethernet switching and aggregation, High-
speed and low-cost transmission links, and Test and performance monitoring. The module or
subsystem tests are also covered there, in order to prepare the integrated tests planned for WP5.
Finally, in Section 5, further architectural aspects are considered.
2 Architecture overview A high capacity transport infrastructure is vital for efficient mobile network operation. While
capacity can be relatively easily increased to accommodate 2G, 3G and 4G Mobile generations, the
needed throughputs for the coming 5G networks will be a technical challenge. The promises of 5G
are expected to enable a “fibre-like” user experience, in its capacity to support the anticipated
requirements such as high throughputs, and low latencies etc. In that context, optical networks are
therefore expected to be the predominant technology for carrying the traffic to the antenna sites
with adequate bandwidth and quality of service (QoS). In the fixed access network segment,
Ethernet is the dominant protocol and interface technology for the Digital Unit (DU) backhaul of 2G,
3G, and 4G due to its capability for supporting QoS policies, aggregation, embedded synchronization
traffic, and security features. Optical access networks based on point-to-point and point-to-
multipoint topologies supports the current Ethernet-based mobile backhaul. In this overview, we
propose to present in a few words the different technical options available to the mobile Ethernet
backhaul for the access network (last mile) segment, in synergy (or not, as the case may be) with
Fibre-To-The-Home (FTTH) deployment dedicated to residential customers.
Figure 1: Optical access solutions for backhaul based on 1) PtP, 2) T(W)DM PON, 3) PtP WDM PON
Contract No: 644526 iCIRRUS 1 Jan 2015 – 31 Dec 2017
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This project has received funding from the European
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programme under grant agreement No 644526
In contrast to the backhaul, the Radio Access Network (RAN) introduces new kinds of network
segments where optical fibre is becoming essential: the midhaul and fronthaul segments. We have
already described in the previous iCIRRUS deliverables D2.1 and D2.2 the network requirements,
whilst here we now briefly put the backhaul evolution into its perspective, and how these new
network notions with their related architectures along with the fixed optical access solutions can
work together to support the fronthaul. Finally, we focus our interest on the latest evolution trends
for RAN, particularly with respect to virtualization features. The evolved fronthaul based on Ethernet
is also discussed in the context of the RAN functional split. Finally, potential technical solutions for
future optical access networks are also presented to support this RAN evolution.
2.1 Access Network Solutions for Ethernet Backhaul
Mobile Backhaul is the transport network between the antenna cell site and the Base Station
Controller (BSC) or Radio Network Controller (RNC), respectively for 2G and 3G RAN. With 4G, there
are no radio network controllers, since controller functions have been incorporated into the evolved
Node B (n.b., eNodeB becoming synonymous with the Digital Unit) for the main part and also within
the serving gateway. Thus, the 4G RAN backhaul is the transport network segment between the
antenna cell sites up to the Evolved Packet Core (EPC). Here, we will focus our interest on the last
mile of this transport backhaul, such that we do not assess the technical discussions about the
aggregation backhaul network based on ring or mesh topologies with typically Internet
Protocol/MultiProtocol Label Switching (IP/MPLS) transport. Figure 1 shows three different technical
solutions based on:
1) A dedicated fibre supporting a point-to-point (PtP) connectivity between the access node,
equipped with an Optical Line Terminal (OLT), and the antenna site, equipped with an Optical
Network Unit (ONU). This PtP topology is the most straightforward solution for areas with plenty
of optical fibre resources.
2) For areas with available (but limited) optical fibre resource, a technical solution is required to
enable it to be shared. The most common and widespread deployed solution is that of Gigabit
capable Passive Optical Network (G-PON). Following the legacy G-PON, it is also widely
recognised that XGS-PON (PON working at 10Gbit/s downstream and 2.5 or 10Gbit/s upstream) is
the next deployable solution for an enhanced capacity fixed broadband, but now also for the RAN
backhaul. All these solutions are based on a wavelength channel pair to achieve the up- and
downstream and which are able to coexist on the same Optical Distribution Network (ODN). Time
Division Multiplexing/Mulitple Access (TDM/TDMA) is used for sharing the trunk part of the ODN
equipped with an optical power splitter at the branching node, and a single optoelectronic
interface at the access node. Since 2015, multi-wavelength PON solutions have been
standardized, either mixing the time and wavelength dimensions (TWDM-PON), or only
considering multi-wavelength (PtP WDM-PON) approaches.
3) This last technical solution based exclusively on Wavelength Division Multiplexing (WDM) could
support either a wavelength-routed topology, where the branching node is composed of a
wavelength multiplexer (WM) device such as an Arrayed-Waveguide Grating (AWG) or a
combination of thin-film filters (TFFs), or a wavelength-selected topology where the branching
node is composed of an optical power splitter or a bandpass wavelength filter. These PtP WDM-
PON solutions also require multi-transceiver interfaces at the access node.
It is mandatory that these three access solutions are based on a single fibre. In other words, the up-
and downstreams operate in bidirectional diplex mode in the fibre, with each ONU being associated
with a wavelength channel pair. In the case that wavelength is used in the access network (i.e. PtP
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This project has received funding from the European
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WDM-PON, or TWDM-PON), a colourless optical module at the ONU capable of working at any
wavelength is essential. All these wavelength channels need to be controlled by the OLT to fix and
control the wavelength allocation and avoid any rogue wavelength behaviour in the concerned ODN.
For any access solution, the ONU must also wait for the OLT’s permission to start emitting optical
power, in order to be compatible with the mandatory “silent start” function. The power
consumption policy must also be optimized under control of the OLT, whereby the OLT is designated
the unique source of management, wavelength and time control and synchronization of the ONUs.
2.2 Fixed Access Network segments for RAN fronthaul The term midhaul has been defined by [3] as the carrier Ethernet network between antenna sites
(especially when one site is a small cell site). The MEF reference scenario in Figure 2 shows that the
midhaul is considered as a backhaul extension between a small cell DU and its master macro-cell DU.
Two other scenarios are also considered: i) the midhaul between two DU pools (illustrated in Figure
2); and ii) the midhaul between two DU pools through a network controller (not illustrated in Figure
2). All midhaul scenarios are Ethernet-based, and use the same fixed access connectivity as the
backhaul.
Figure 2: Mobile Backhaul, Midhaul and Fronthaul from MEF [3].
Figure 3: First three steps of mobile equipment evolution.
The term fronthaul [4] is used to designate the dedicated connectivity between the Digital Unit
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This project has received funding from the European
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(DU) and the Radio Unit (RU). In the sections 3 and 4, we will only focus our interest on this fronthaul
segment. Figure 3 shows the different RAN arrangement scenarios based on short and long reach
fronthaul scenarios. The radio signal processing functions are commonly supported by equipment
localized at the base of the antenna. Concerning the RF amplifier inside the RU, we note that its
performance (power consumption and cost) depends on the RF attenuation of the coaxial cable
reaching the antenna (cf. Figure 3a). It is desirable that an outdoor form factor for this RU remains
close to the antenna. As far as the links between DU and RUs are concerned, the main requirements
are to allow for the lowest RF signal degradation possible and to reach several tens of metres of
propagation. The combination of DAC & ADC (Digital to Analog Converter, and vice versa) and digital
transmission over fibre with regular pluggable optoelectronic transceivers has allowed the meeting
of these requirements (cf. Figure 3b). This approach to splitting the RAN equipment is known as low
layer RAN functional split fronthaul, and uses CPRI, OBSAI or ORI interfaces [8]. Each fronthaul link
between DU and RU is based on a constant and symmetrical high bit rate serial digital interface
created from the digitization of the baseband, time domain radio signals. Typically, three times 2.5
Gbit/s (i.e. 3x2.5-Gb/s) are needed to transport a 20 MHz 2x2 Multiple Input Multiple Output
(MIMO) radio signal for three sectors whose maximum mobile peak bit rate is limited to about 150
Mbit/s. The reason behind such poor spectral efficiency can be straightforwardly explained by the
quantization and coding operations needed to convert the radio signals into the on-off keying (OOK)
sequences used in the optical link. Also, the clock of this OOK signal serves as a reference for mobile
RF generation inside the RU. Since commercial optical transceivers are available and can reach
several tens of kilometres at the required bitrates, the reach extension of the fronthaul becomes
possible (cf. Figure 3c). Optical fibres are used to reach the antenna sites within the limit of the
maximum round trip time allocated to the fronthaul (typ. 20 km for one way, given that this value
depends on the RAN implementation). Nevertheless, the feasibility of transporting these fronthaul
links over Ethernet network equipment remains a challenge due the bit rate quantity and the timing
requirements. The IEEE launched a standard specification action to draft a standard for Radio-over-
Ethernet to achieve encapsulations and mappings of Inphase/Quadrature (I/Q) radio samples
coming from variety of fronthaul interfaces. The IEEE document P1914.3, which addresses this issue,
is still under construction [5].
Figure 4 shows the most common fixed access solutions for the last mile transport of the native
low layer fronthaul without Ethernet encapsulation. The first one uses a dedicated optical fibre per
RU. We can see from Figure 4a that several fibres are required to reach all the RUs (one per sector,
per radio carrier, per radio technology). In order to achieve fibre sharing, PtP WDM-PON could be
used (cf. Figure 4b). The passive wavelength-based access solution is compatible with the required
line rate and low latency (no framing) by using colourised transceivers at the DUs and RUs.
Contract No: 644526 iCIRRUS 1 Jan 2015 – 31 Dec 2017
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This project has received funding from the European
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programme under grant agreement No 644526
2.3 New Fixed Networks segments for virtual RAN
Nowadays, a new trend is paving the way for 5G RAN architectures based on different functional
splits. Rather than concentrating all the network intelligence at the DU, such as is the case with CPRI,
OBSAI and ORI, these splits consist of transposing some of the radio protocol layers to the antenna
site. Different possible functional split have been proposed and analysed, based on three main
drivers: i) the feasibility to implement some of the DU functions by software [6], which allows one to
dynamically change and optimize the functional split between a (now) virtual DU and RU, hosting the
real-time dependent hardware; ii) to provide an alternative to the bandwidth hungry and time
sensitivity of the current existing fronthaul solutions; iii) to reuse the widespread Ethernet-based
access ecosystem.
Putting these drivers together, a solution has appeared that is based on high protocol layer
functional splits. This choice allows a virtual DU (v-DU) to be placed at an edge node and to be
connected via Ethernet (access and aggregation network segments) to e-RUs (“e” for
evolved/Ethernet RU). Figure 5 shows such RAN evolution in combination with the already
mentioned fixed optical access solutions [7]. Similar to the previously shown fixed access solutions
for backhaul, we find once more that solutions are based on the following: (n.b. we remind that we
are not considering the aggregation network in this introduction)
1. A dedicated optical fibre between the access node and the antenna site, with the particular
aspect that the ONU must now collect (and prioritize) multiple Ethernet traffic from/to several e-
RUs.
2. A shared optical fibre using a T(W)DM-PON solution with a dedicated or shared ONU to collect
several e-RUs. If high layer Ethernet fronthaul traffic prioritization is required, this functionality
could be done by the OLT with a specific dynamic bandwidth allocation mechanism.
Figure 4 Optical access solutions for low layer fronthaul based on a) PtP fibre, b) PtP WDM PON
Contract No: 644526 iCIRRUS 1 Jan 2015 – 31 Dec 2017
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This project has received funding from the European
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programme under grant agreement No 644526
3. A shared optical fibre using a PtP WDM-PON solution, also with dedicated or shared OLT to
collect several ONUs. The use of several wavelength channel pairs could allow the minimization
of Ethernet traffic congestion in the transport equipment, by implementing this function in
dedicated Ethernet equipment in either the access node or at aggregation network segments.
This transport solution also allows (on the same fibre) the support of distinct traffic with
different policies, such as backhaul and low- or high-layer fronthaul for different RAN carriers or
different (i.e. legacy) generations of RAN equipment. This would also provide a seamless
migration path.
2.4 Fronthaul deployment options In the previous section 2.2 and 2.3, the fronthaul deployment options have been already described
for the transport of a low and high layer RAN split. In this section we now propose to comment on
some of the key points for each of the transport RAN segments:
Backhaul: the preferred deployment scenario is based on PtP Ethernet. The option of using TDM-
PON (like G-PON) is that a synergy with FTTx is technically feasible, although not largely used due to
the following reasons:
- Availability of specific ONUs;
- Complex inventory and maintenance due to different skills and group involved from fixed and
mobile operation;
- RAN traffic is not guaranteed (congestion in combination with fixed traffic).
If G-PON were the adopted technology for the 2G, 3G and 4G backhauling, then that makes the XGS-
PON more appropriate for the 4G+ and 5G phase 1 (carriers at 700MHz and 3.5GHz) evolution. The
25G-PON and TWDM-PON would then be the most appropriate PON technology for 5G phase 2
(mmW carriers).
Figure 5: Optical access solutions based for high layer fronthaul based on: 1) PtP, b) T(W)DM-PON, 3) PtP WDM-PON.
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Low layer RAN split
The preferred deployment scenario is dark fibre with the option to use WDM-PON for fibre sharing.
This network segment is short range due to the RAN latency requirements.
High layer RAN split
This interface is “similar” to the backhaul, in term of the transport protocol (Ethernet), throughput
and latency. Hence, an existing backhaul solution which is natively based on PtP Ethernet will also be
the preferred option in order to maintain the existing operation and the reduced cost aspects.
Nevertheless, due to the required 5G throughput (and latency), the combination of multiple 5G RAN
equipment (several carriers are coming for 5G phase 1 and phase 2) and the need to reuse the
existing backhaul fibre, the option of point-to-point for several parallel Ethernet links using WDM, is
also now on the table for 5G. If TDM-PON for backhaul is adopted, the XGS for 5G phase 1, and 25G-
PON and TWDM for 5G phase 2 will also be the potential technology for high layer RAN split
transport in the access segment.
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3 Refined fronthaul requirements and KPIs
3.1 Data rates
3.1.1 Data rate for low layer RAN functional split
The required bandwidth capacity with different communication standards scenarios for a single base
station is shown in the table 5-1 below for the low layer RAN functional split.
Table 1: Overview of required CPRI line rate as a function of several RAN configurations
Radio access technologies (3
sectors)
CPRI line rate for
downlink only
configuration of the fronthaul
interface
LTE (20MHz & 40MHz with 2x2 MIMO) 3×2.5 = 7.5 Gbit/s &
3×5 = 15 Gbit/s
RUs without cascading:3 parallel links
working at 2.5 or 5 Gbit/s
RUs cascaded:one link working at 10 or
25 Gbit/s
W-CDMA (20MHz with SISO) 3×1.25 Gbit/s =3.75 Gbit/s
RUs without cascading:3 parallel links
working at 1.25 Gbit/s
RUs cascaded:one link working at 5
Gbit/s
GSM (10MHz with SISO) 3 x 614.4Mbps
RUs without cascading:3 parallel links
working at 1.25 or 2.5 Gbit/s due to the
fact that it is comon to fill the pattern with
idle bits in order to use the most common
1.25 or 2.5Gbit/s optical transceiver
RUs cascaded:one link working at
2.5Gbit/s
two RF carriers of
LTE(20MHz)+WCDMA+GSM
2 x 3 x 2.5 Gbit/s + 3 x 1.25
Gbit/s + 3 x 614.4 Mbit/s = 20.5
Gbit/s
RUs without cascading: 12 parallel links
working at 2.5 Gbit/s (uniform transceiver
line rate by using idle in the CPRI pattern)
RUs cascaded:4 parallel links working
at 10 (for LTE), 5 (for WCDMA) and 2.5
(for GSM) Gbit/s
This table shows that parallel links are required for the low level RAN functional split. This is due to
the fact that without cascading RUs, a bidirectional link needs to be provided for each sector and the
RF carriers. However, with cascaded RUs, the line rate of a link increases. Consequently, many
studies are now being undertaken to reduce the line rate on the fronthaul connection by introducing
compression and/or a different functional split between DU and RU.
Essentially two families of approaches are being considered in the literature:
o Compression techniques, which reduce the line rate at the expense of some loss of
information and additional delay. ETSI proposes that the IQ compression solution should
achieve a compression ratio of at least 50%, with an EVM degradation and a one-way
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supplementary latency caused by the compression/decompression algorithms of under
3% and 100µs (preferably 20µs), respectively,
o New functional split options, which modify the hardware split between central and
remote units at the expense of having new layouts for the remote units and changing
the interoperability paradigm.
Both approaches enable significant savings in terms of the capacity of the RAN interfaces. A
compression technique is described in [9] with a complete description of the functional split options.
The next section also discusses the requirements of these high levels RAN split options.
3.1.2 Data rate for High layer RAN functional split
We report in this section the potential network requirements of several high layer RAN splits as
shown in Figure 6.
Figure 6: Several high layer split fronthaul interfaces for the downlink and uplink.
Since these new functional splits are not defined yet by a standardisation document, the proposed
parameters of this white paper could be modified in the future. We propose also in this section to
position these high layer function splits [10] (named F1) with respect to the existing and well-known
RAN interfaces which are the backhaul (S1) and the currently adopted low layer split based on CPRI
or OBSAI.
Table 2 is based on a small cell forum analysis [11] and shows the latency and data rates of these
functional split interfaces, considering a 20 MHz channel bandwidth and 2x2 MIMO for downstream
and 1x2 MIMO for the upstream (more parameters are defined in Appendix C: Bandwidth
calculations of [11]). This data calculation for a split at the high layers, e.g. between PDCP and RLC or
RLC and MAC layers, highlights some interesting facts, namely:
o The traffic is dependent on the traffic load of the end users (i.e., it is not constant traffic
as is the case for low layer splits, e.g. CPRI/OBSAI and split PHY);
o Extra traffic must be considered in addition to the traffic associated to the traffic load of
the end users. This extra traffic between the modified DU and RU is used to achieve
control, scheduling, and security & synchronization features. An extra +10% of traffic
(with a maximum of 100 Mbit/s) must be used, in the first approximation, to dimension
for such features. An exact value still needs to be specified, and this is a function of radio
standardization efforts and also vendor implementation. Some of the extra traffic could
also be constant (i.e. independent of the traffic load of end users).
These high layer functional split interfaces are compatible to packet flow and thus Ethernet will be
the common protocol used. We note that the IEEE working group P1914 “Next Generation
Fronthaul Interface” is expected structure the support of Ethernet for some of these high layer
functional splits.
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Table 2: Data rate for different RAN functional split (20MHz, and 2x2 MIMO for downstream and 1x2
MIMO for upstream)
RAN functional split (cf.
Figure 6)
One-way latency
(maximum value)
Downlink
data rate
(Mbit/s)
Uplink
data rate
(Mbit/s)
Comments
backhaul 30 ms 150 48.5 Security and synchronization features
are required
service (RRC-PDCP) 30 ms 150.1 48.6 Traffic dependent on customer
demand
Plus 10% s extra traffic versus
backhaul for control, scheduling,
security, and synchronization (not
included here)
PDCP-RLC 30 ms 150.3 48.7
MAC 6 ms 151.5 49.4
MAC-PHY 2 ms 152,5 49.9
Split PHY (between
resource mapping and
FFT)
250 µs 1075.2 921.6 Constant traffic (not traffic load
dependent)
Synchronization natively include
CPRI/OBSAI (low layer
split)
250 µs 2457.6 2457.6
Additionally, from a mobile operator perspective, a split at the higher layers, e.g. between PDCP and
RLC or RLC and MAC layers, might be of interest. Such an Ethernet backhaul-like traffic forwarding
solution might offer a potentially easier and faster deployment. However, it is not capable of
supporting advanced radio interference techniques such as inter-site CoMP. It is also necessary to
pay attention to the traffic dimensioning of the optical access segment. This traffic dimensioning
refers to all the aggregation equipment, and could be different compared to what is used in the
current backhaul where a statistical multiplexing factor (typically between 3 and 5 for a macro cell
site with 3 sectors) is used. The statistical multiplexing factor can be applied at every point in the
access network that aggregates and combines Ethernet interfaces from multiple sources (e.g. also
from different backhaul mobile generations, or from several 5G RUs each equipped with an Ethernet
interface). What needs to be noted is that for traditional wireless backhaul, the statistical
multiplexing factor is applied at the L3 interface of the RAN equipment.
Nowadays, for the Ethernet transport of the high layer split, and due to the fact that L3 RAN is not
already deployed, the dimensioning and aggregation rules should also now be defined with attention
to the end-to-end quality of service (QoS).
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3.2 Fronthaul Security
3.2.1 Introduction to Security in 5G Access Networking
Future 5G networking features both wireless and fixed links, each of which has its own security
issues. The broadcast nature of the wireless domain and mobility of the users make them
susceptible to a wide variety of security attacks such as passive (Traffic analysis and Eavesdropping)
and active (Denial of service (DoS) attacks, Resource consumption, Masquerade attacks, Replay
attacks, Information disclosure and Message modification) attacks [12]. Traditional solutions to
mitigate the security challenges are usually handled at the upper layer using various types of private
and public secret keys via computation-based mechanisms (i.e. cryptography). The reliability in the
exchange of information between a source node (commonly denoted as “Alice") and an intended
destination node (commonly referred to as ”Bob"), and security in terms of confidentiality and
message integrity with respect to an adversary (commonly referred as “Eve") using the
computational security approaches have been reported, e.g. in [13][14] to be susceptible to attacks,
and in a dynamic mobile environment it is computationally complex and intensive [15][16]. Security
is seen from the viewpoint of layered network design as an add-on feature, in particular separating
the physical layer from the upper layers as a reliable bit pipe, and providing security at this layer
should be viewed as fortifying the existing computational security techniques.
Physical layer security (PLS) against passive and active attacks is classified into five major categories:
theoretical secure capacity, channel, coding, power and signal detection techniques. The
fundamental issues of theoretical secure capacity have drawn much attention and most of the works
in this area focuses on secrecy capacity; that is, the maximum rate achievable between the
legitimate transmitter-receiver pair subject to the constraints on information attainable by the
unauthorized receiver. In summary, information-theoretic security is an average-information
measure and it also requires the knowledge of the channel state information that may not be
necessarily accurate in practice. The channel approach is classified into three methods to provide
physical layer security based on exploitation of the channel characteristics, such as radio frequency
(RF) fingerprinting, Algebraic Channel Decomposition Multiplexing (ACDM) pre-coding, and
randomization of MIMO transmission coefficients. The main objective of the code approach is to
improve resilience against jamming and eavesdropping. The code technique is subdivided into the
use of error correction coding and spread spectrum coding. Information protection can also be
facilitated using power techniques. The usual schemes here involve the employment of directional
antennas and injection of artificial noise. A directional antenna facilitates receiving of data from the
direction not covered by the attacking signal. Finally, in the case of introducing artificial noise, secret
communication between legitimate nodes can also be achieved. In [18], a method has been
proposed in which discriminatory channel estimation is performed by injecting artificial noise into
the remaining space of the legitimate receiver's channel in order to degrade the estimation
performance of the eavesdropper. An improvement to this approach is discussed in [19], whereby
the channel feedback information from the legitimate receiver at the beginning of each
communication stage is exploited, and this is called a multi-stage training-based technique.
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3.2.2 Security Considerations for 5G Architectures
Security for a 5G hierarchical architecture presents a technical challenge as compared to the more
centralized 4G network architecture. A distributed e2e security approach depends on the algorithms
and methods implemented at the end-points of a connection, i.e. the user devices (UEs); as well as
those algorithms and technologies located between user devices and the services offered by the
network. To provide e2e security, user data needs to be encrypted at the mobile device, e.g. each
previous mobile network generation has used a different encryption method: the A5/1-A5/4
methods were used in GSM; 128 bit encryption and the KASUMI algorithm is used in 3G; while
SNOW/AES is used in 4G/LTE. A generic architectures view of 4G (centralised) and a decentralised
(hierarchical) 5G network is shown in the Figure 7 below.
Figure 7: Comparative architecture views of 4G (centralised) and 5G (decentralised) networks.
While UEs are authenticated in the EPC, they communicate via unsecured transport networks. In
addition, because of the flat 4G IP architecture, communication between eNodeBs and core network
is not authenticated. The Access Stratum is terminated in the eNodeB and thereby protects control
and data transport over the air interface. The user plane and control plane traffic between eNodeB
and EPC is, however, not protected in the Non-Access Stratum, with only the encryption keys and
the control traffic between eNodeB and Mobility Management Entity (MME) being protected.
Communication between decentralized eNodeBs and centralized EPC is realized, in general, via the
unprotected transport network, which consists of the fixed access-, metro- and core networks that
are partly or fully shared among multiple operators and services. 4G/LTE user traffic can therefore
be tapped most easily within unsecured microwave links, which are frequently part of the fixed
access network. 4G LTE networks represent a centralized core network architecture, with the radio
access network being entirely distributed. All base stations (eNBs) are connected via an IP-based
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network to the central node/cloud, which performs the functions of the IP packet core (EPC) and the
MME, which includes the routing.
5G networking is causing EPC functionalities to be shifted closer to the base stations; so that the EPC
is becoming less complex and more distributed in nature. However, the many small µEPCs therefore
need to be supervised by a central EPC/MME control network function. From the security point of
view, distributing the EPC functionality is an improvement because distributed security is harder to
attack. The µEPC can be much closer to the base stations, so that the path lengths are thereby
shorter and latency also significantly reduced.
Security improvements have also been suggested for 5G C-RAN architectures, such as iCIRRUS, which
features functional splitting of the eNodeB into a radio unit (RU) and a more centralized baseband
unit (BBU) or DU. In this way, numerous RUs are attached via a new fronthaul interface to one DU,
which allows significant improvements for interference management. Towards the core network,
the DU behaves like a giant base station, having a large number of distributed antennas. Hence, in
5G the DU also has the S1 F1 and Xx interfaces, analogous to the eNodeB in 4G LTE.
While the backhaul behind the eNodeB can be unsecured in 4G LTE (e.g. if a microwave link is used),
the compound link from the UE over the air to the RU and then via a microwave or fixed network
connection to the centralized DU is inherently secure. Only after the DU, is a secure uplink to the
µEPC needed again. The cloud-based iCIRRUS architecture enables instantiation of virtualized core
and RAN network functions among the clouds. In a shared network infrastructure, this implies that
these network functions have to be encapsulated by a secure transport protocol, such as IPSec.
Operators using the shared physical substrate of an infrastructure provider hence have to build
secure islands inside each cloud, which are isolated from the other operators using the same
physical substrate, and where their own virtual network functions (VNFs) can be operated. The only
function that needs no isolation is that of routing, which is natively safe when using IPSec. But all
other VNFs will need security encapsulation.
The cloud infrastructure is a remaining security weakness in the cloud-based iCIRRUS infrastructure,
because isolation between the tenants is virtual; but VNFs of different tenants can be physically
processed in the same machine. At the low processing level, tenants are therefore not physically
isolated. One way out is to only use certified cloud hardware in which interactions between the
tenants can be considered to be impossible. As a result, a combination of over-the-top (OTT) and
network-assisted end-to-end security enables the shared use of the 5G network infrastructure, while
guaranteeing low latency, with secure end-to-end communication.
Targeting an all-IP infrastructure also forces network designers to be aware of secure layer 3 and
layer 2 communications. From a security point of view, authentication and data integrity needs to be
provided by the network infrastructure, to provide data security beginning from the lowest level
possible.
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3.3 Fronthaul KPIs The following Table 1 gives an overview of the fronthaul KPIs.
Table 1: Compilation of Fronthaul KPIs
4 Updated architectural building blocks
4.1 Ethernet mapping and encapsulation LTE functional subdivisions or “splits” have been considered as a means of meeting fronthaul data
rate requirements for next generation mobile networks, and as such have attracted the interest of
standards bodies including both 3GPP and IEEE groups. A number of potential split points have been
identified with factors such as data rate, latency, ease of migration/deployment and ability to
accommodate advanced joint signal processing techniques, playing an important role in the choice.
A number of possible split options are shown in Figure 8(a) using the NGMN numbering scheme. In
general, split points further away from the antenna and towards the mobile core, offer the highest
reductions in data rates while starting from the radio side and moving towards the core, a number of
interesting interface points can be identified. The different LTE channels are demarcated at the
resource mapper (RM) (Split II), resulting in an aggregate data rate that depends on the cell load,
leading to statistical multiplexing gains. At the antenna-processing block (layer and port mapper) the
transition from per-antenna flows to per-user flows occurs (Split I), resulting in large reductions in
data rates as these stop depending (proportionally) on the number of antennas. In general,
frequency domain splits lead to data rates through reductions in sample widths (bits per sample)
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and sampling redundancy (time domain oversampling). These data rate reductions need to be
considered in unison with all the other performance factors of a given split point (latency, support
for CoMP, pooling and virtualisation gains), with splits closer to the radio side more able to
accommodate advanced joint processing features and larger virtualization/pooling gains.
The MAC/PHY split offers a good overall balance at the expense of the strict latency constraints that
the Ethernet fronthaul will need to meet. Ethernet features such as prioritized scheduling, may offer
means for guaranteeing timely delivery of packets to/from the RU.
The implemented MAC/PHY split is shown in Figure 8(a). The split interface resides between the
MAC layer processing, and the error correction block. The resulting processing module subdivision is
shown in Figure 8(b). The LTE eNodeB protocol stack, up to and including the MAC layer, runs within
the DU and generates MAC layer protocol data units (PDUs) (or MAC transport blocks (TBs)). The
PDUs are encapsulated into Ethernet packets, sent over the Ethernet network and received by a
remote aggregator unit (RAU) which de-packetizes the PDUs and performs all the physical layer
processing (forward error correction (FEC), quadrature amplitude modulation (QAM), antenna
processing, mapping of resources to resource blocks and inverse-fast Fourier transformation (IFFT)).
The resulting IQ radio samples are sent to the remote radio head (RRH) for radio frequency (RF)
processing. The RAU and RRH together then form a remote unit (RU).
The networking entity subdivision is shown in Figure 8(c). The EPC runs in a separate processing
node that is connected through GbE (gigabit Ethernet) to the DU, which in turn is connected to the
RAU through GbE.
The testbed is flexible and can run with different options of emulated, simulated or real hardware
implementations. For example, it can include the EPC, hardware-based RF (e.g. Universal Software
Radio Peripheral , USRP) and commercial 4G phones. Alternatively, it can employ emulated UEs and
simulated air interfaces with and without S1 interface.
For the results presented here, the EPC, DU, RU and UE entities are software emulations, while the
RF processing and air interface are simulated. There is no S1 and F1 interface, and instead internet
protocol (IP) data is fed directly at the PDCP layer at the DU.
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Figure 8 (a) Different LTE functional subdivisions (function splits) options, (b) The implemented split processing module
subdivision and (c) the implemented split networking entity subdivision.
A different view of the fronthaul network, concentrating more on the networking part and the
subdivisions of the traffic flows is shown in Figure 9. The LTE functionality in the DU, RU and user
equipment (UE) runs in a software emulation environment based on the open source
OpenAirInterface (OAI) software libraries (see the OpenAirInterface software alliance) and
specifically on the ‘OAI5G’ source code. A Fronthaul Interface Library (FIL) is used to encapsulate the
data exchanges between the functional split entities and to provide a useable abstraction (mapping
functions) to the new functionality. The DU performs all the eNodeB processing up to and including
the MAC layer. It then generates a number of flows and packetizes them into the MTU section of an
Ethernet frame. The resulting packet-types include, downlink shared channel (DLSCH), downlink
control information (DCI), system information (SI) and random access response (RAR). The flows are
then transported over an Ethernet network and are received by the RU, which performs all the LTE
physical layer (PHY) processing. For simplicity, in the uplink (from RU to DU) a single packet type is
used to aggregate all uplink transmissions. The network comprises standard Ethernet switches
forming trunk links where different data flows can contend. A background traffic generator is used in
the figure to indicate how contention with background traffic can be tested in a testbed
environment. The PKT_DCI packet is processed at the RU before retrieval of any of the other packets
is attempted, as this packet, in addition to carrying the DCI data, also acts as a MAC/PHY primitive
carrier. The primitives are used by the RU to extract information on the type and number of
allocations to expect for the current subframe.
The encapsulation format used is common to all packets and is shown in Table 2. The system has the
flexibility to identify flows at varying “resolutions” by combining VLAN IDs and packet-types. An
example configuration entails “bundling” all packet types destined for the same RAU, within the
same VLAN. The standard Ethernet headers are indicated in Table 2 using italicised fonts. The other
headers form part of the Ethernet payload, which means that the network socket at either end of
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the fronthaul is aware of the header boundaries. A number of these header values are used in the
buffer management algorithm at the two end-points of the fronthaul network. The Ethernet payload
section contains packet-type specific data (fields) in addition to the MAC PDU. An example is shown
in Table 3 for the PKT_DLSCH packet.
Figure 9 (a) The evolved fronthaul and (b) high-level view of the buffering stages and measurement interface points
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Table 2: The 28 (32)-Octet common packet header for all packets sent/received through the fronthaul interface.
Field Size/Octets Description
Dst MAC 6 The destination H/W address, source H/W
address, and EtherType - as per IEEE 802.3.
EtherType is fixed to hex ’08 00’ alluding to
IPv4 datagram.
Src MAC 6
VLAN ID (Optional) 4
EtherType 2
SFN (TX) 2 The LTE SFN and subframe the data in the
packet is part of (for Tx processing). LTE Radio Subframe
(Tx)
1
SFN (RX) 2 The LTE SFN and subframe the data in the
packet is part of (for Rx processing). LTE Radio Subframe
(Rx)
1
Packet-type 2 An unsigned 16-bit enumeration of the packet
types.
Packet Length 2 The size of the packet in Octets, as an unsigned
16-bit integer.
Payload N Packet payload including packet-type specific
data (see Table 3 for example, for PKT_DLSCH)
CRC 4 Cyclic redundancy check
Table 3: Ethernet frame payload fields for PKT_DLSCH
PKT_DLSCH (Ethernet Frame Payload Section)
Field Size/Octets Description
UE index 1 Index of the UE the data in the packet is
intended for
RNTI 2 UE Cell radio network temporary
identifier
Length 2 Length of the payload
Payload N-5
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Figure 10 shows a comparison of the data rates produced by the application layer (here using the
OAI traffic generator) and those over the fronthaul interface, for different numbers of UEs. The
downlink application data rate per UE is approximately 1.2 Mb/s while the uplink data rate is entirely
due to control data, and LTE and FIL encapsulation overheads. The ‘total overhead’ trace shows the
increase, as a percentage, between the application and fronthaul data rates, and is approximately
43% for the different number of UEs. It is due to the encapsulation overheads added by the LTE
protocol stack and the FIL.
Figure 11 shows the results of three different tests for a single UE, with each test representing a
different data rate from the OAI traffic generator (OTG). Each processing stage adds some overhead,
resulting in a higher data rate. The first stage includes the MAC PDU encapsulation with a resulting
data rate increase of 34% and is a result of the addition of all LTE headers (PDCP, RLC and MAC).
Following this stage, the PDU is processed by the FIL with a resulting overhead increase of 3%. Both
of these increases are constant for all three tests, as the DLSCH size is fixed to approximately 1000-
octets. The last stage includes all packet types transmitted over the fronthaul, and in this case, the
percentage increase varies form one test to the next. While the amount of SI data is independent of
application data rate, the amount of DCI data is not resulting in a different percentage increase in
each test.
Figure 12 shows the subframe processing latency of the FIL for different numbers of UEs. The latency
is measured between the first packet in each subframe (a DCI packet-type) at interface point 3 and
the last packet for that same subframe at interface point 3 (see Figure 9 (b)). The latency increases
approximately linearly with number of UEs.
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Figure 10 Fronthaul and application (OTG traffic generator) data rate measurement results for different numbers of UEs.
The traffic generator is producing traffic only for the downlink direction.
Figure 11 Data rates and percentage increases at different points in the processing chain, for three different tests of
ascending application layer data rates.
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Figure 12 Fronthaul processing latency per LTE subframe for different numbers of UEs
4.2 Timing and synchronization (priority/scheduling) The combination of functional splitting and Ethernet means that new traffic mechanisms will
become available due to statistical multiplexing gains in aggregation nodes (Ethernet switches).
However, this means that fronthaul links will need to be provisioned for the timely delivery of
fronthaul traffic to the end stations. To this extent, IEEE 802.1 CM [20] is defining/adapting time-
sensitive networking (TSN) profiles for fronthauling. Currently focusing on CPRI flows, similar profiles
will be required for functional split traffic and any in-line timing protocol (e.g. PTP).
Previous work has investigated the effects of using priority based scheduling in the fronthaul for IQ-
based traffic using weighted round-robin (WRR) and strict priority (SP) [21] algorithms. The latter is
attracting more attention recently as it is (tentatively) part of the IEEE 802.1CM profile A [20].
Additional techniques include frame pre-emption (IEEE 802.1Qbu) [22] and time-aware shaping
(IEEE802.1Qbv) [23]. For the latter, simulation results with CPRI [24] and traffic flows emulating
functional split and PTP traffic [25] have been presented.
4.3 Fronthaul intelligent processing unit (IPU) The purpose of the fronthaul intelligent processing unit (IPU) is to Collect information from the
fronthaul and over-the-air (OTA) sections, run the algorithms that extract the necessary metrics and
produce statistics for the different KPIs. These statistics are in turn used to adapt the operation of
the fronthaul and OTA sections by employing software-defined networking (SDN) techniques and
handovers for load balancing. The IPU also reports the statistics in real time and presents them in a
user-friendly manner.
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Figure 13 The fronthaul intelligent processing unit (IPU) and the different feeds from the fronthaul and OTA sections
The PacketPortal Python API is used to collect metadata from the filtered result packets (FRPs) and
metric result packets (MRPs). These include timestamps, probe IDs, sequence numbers and packet
counts. The Dynamic KPI extraction block is then used to calculate the different KPI statistics which
include inter-frame delay, latency, frame-delay variation (FDV) and throughput.
The SDN controller VM runs the POX2 controller. The controller sets the initial flow table
configuration in the SDN switch and thus operates in a pro-active manner. That is, the switch does
not have to send packets from newly received flows to the controller so that its flow tables are set,
thus avoiding this initial set-up delay. The controller is fed average KPI values (e.g. for latency) by the
KPI extraction VM and once a KPI value exceeds a certain threshold in triggers traffic steering to take
place by steering a flow (or flows) to another trunk link. The controller also has the ability to obtain
KPIs from the switch (e.g. number of packets, throughput) and steer based on these. An example of
traffic steering in an evolved fronthaul is shown in Figure 14. Note the latency variation due to
contention with background traffic and the corresponding significant reduction in both latency and
latency variation once the steering occurs.
2 http://searchsdn.techtarget.com/definition/POX
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Figure 14 The result of SDN-enabled traffic steering of the background traffic in an evolved fronthaul on the latency and
latency variation of the split traffic. Three packet type traces are shown, pkt_DCI (downlink control information), pkt_DLSCH
(downlink-shared channel) and pkt_SI (system information).
The OTA remote API is implemented through a web socket connection between the DU and the IPU.
The IPU continually interrogates the DU regarding a number of KPIs. These include HARQ
retransmissions and cell load among others. The IPU can then send commands to initiate handover
and/or dynamically change the power level of each cell (recursive cell stretching). An example of
dynamic KPI monitoring and how the KPIs are presented in graphical format is shown in Figure 15.
For this example, the monitored traffic is from a centralised split (IQ transport) with an LTE
bandwidth of 5 MHz, while the fronthaul includes one GbE switch.
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Figure 15 Example of live KPI performance monitoring.
4.4 Time sensitive Ethernet switching /aggregation As previously stated, in an Ethernet-based fronthaul for 5G networks, latency and especially latency
variation or packet delay variation or frame delay variation are major issues to fulfil the stringent
timing and synchronization requirements. Time-sensitive networking means are being discussed in
standardization like IEEE 802.1 (see iCirrus Deliverable D3.2 Preliminary Fronthaul Architecture [26]
and Section 4.4.2 below). A a novel approach to provide low deterministic latency for time sensitive
traffic has been identified and investigated as romising technology (see [27] and Section 4.4.1
below).
4.4.1 Deterministic Ethernet transport with low and fixed latency
4.4.1.1 Concept
FUSIONA combination of packet and circuit switching is used to multiplex high priority (HP) traffic
streams (the circuit switched part) with low priority-statistically multiplexed (SM) streams (the
packet switched part) and to transport them over an Ethernet network [27]. The HP traffic is also
called GST (Guaranteed Service Transport). The main idea is to take advantage of the inter-packet
gaps between HP frames to transport low priority (LP) frames of SM streams while leaving the HP
streams essentially unaltered (see Figure 16). As the approach uses the best properties of circuit and
packet switching, it is called “FUSION”.
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Figure 16: FUSION: Exploiting the inter-packet gaps between HP frames to transmit LP frames
This is accomplished by adding a deterministic delay to an outgoing HP stream, which is equal to the
maximum transmission time of a SM frame. A gap detector obtains the inter-packet gaps and an SM
scheduler chooses an SM frame that fits within each inter-packet gap. The receiver extracts the HP
streams with the inter-packet gaps preserved. As a result, latency variation is significantly reduced
(max about of 160 ns, but depends on the number of aggregated streams). Furthermore, this
approach achieves a significantly improved utilisation compared to a fully provisioned circuit
switched network and does not require any additional (out-of-band) form of synchronisation, like is
the case in a time-triggered communication or in a TDMA (time division multiple access)-based form
of communication.
4.4.1.2 Theoretical latency and latency variation considerations
Figure 17 depicts the test setup in order to investigate the FUSION approach.
Figure 17: FUSION: Test setup investigating latency and latency variation
The test setup consists of two aggregator or switch instances, with three 10G Ethernet ports and one
100G Ethernet port. Each aggregator instance uses a FUSION 100G FPGA-based IP provided by the
Norwegian company Transpacket A/S. The presented setup considers of one GST link and two SM
links.
Figure 18 provides an overview of the theoretical estimation of the latency and latency variation of
the FUSION IP.
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Figure 18: FUSION: Theoretical estimation of the latency and latency variation of the FUSION IP
The GST aggregation consists of a variable part depending on the MTU (maximum transfer unit) and
two fixed processing delays (Agg GST fixed processing delay and Agg fixed processing delay). The GST
de-aggregation encompasses only a fixed processing delay (Deagg fixed processing). The SM
aggregation consists of a variable part (depending on the packet size and number of SM streams)
and one fixed processing delay (Agg SM fixed processing delay). The SM de-aggregation is the same
as for GST. The values of the fixed delays provided by Transpacket are dependent on the FUSION IP-
Core Version. The considered FUSION IP-Core Version is V1.5.
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The aggregation case using (one FUSION IP instance) is depicted in Figure 19.
Figure 19: FUSION: Theoretical latency and latency variation for aggregation, neglecting PHY and MAC delays and cable
delays
The table part of this figure describes minimum latency, maximum latency and packet delay
variation. The middle column shows the values, the calculation equation and an example calculation
for GST with parameters for the present test setup (1 GST link, 2 SM links, GST MTU with 16000
Byte). The right column describes the values, the calculation equation and an example calculation for
SM for the used test setup (1 GST link, 2 SM links, SM packet size with 9622 Byte (9600 Byte payload
and 22 Byte header overhead).
Figure 20 shows the aggregation and de-aggregation case using two FUSION IPs that we also have in
the considered test setup (see also Figure 17). The table part of this figure describes values,
calculation equation and example calculation for minimum latency, maximum latency and packet
delay variation for the considered test setup for GST and SM. The middle column depicts GST,
whereas the left column describes SM. Similar to the single aggregation consideration before, PHY
(physical) / MAC (media access control) layer delays are neglected as well as cable delays.
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Figure 20: FUSION: Theoretical latency and latency variation for aggregation and deaggregation neglecting PHY and MAC
delays and cable delays
Figure 21 includes the additional PHY/MAC delay for GST, and Figure 22 presents it for SM. Both
figures provide total end-to-end values for minimum latency, maximum latency and packet delay
variation for the considered test setup without considering cabling. The values for 10G MAC/PHY
and 100G MAC/PHY are measured values on a Xilinx VCU110 platform (Virtex UltraScale XCVU190-
2FLGC2104E). For GST, the minimum latency results in 14175 ns, the maximum latency in 14250 ns
and the latency variation in 95.6 ns. In the case of SM, the minimum latency results in 8769 ns, the
maximum latency in 11177 ns and the latency variation in 2408 ns.
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Figure 21: FUSION: Theoretical end-to-end latency and latency variation including PHY/MAC delays and neglecting cable
delays for GST links
Figure 22: FUSION: Theoretical end-to-end latency and latency variation including PHY/MAC delays and neglecting cable
delays for SM links
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4.4.1.3 Latency and Latency variation measurements
To check the compliance of the theoretical considerations with real systems, a lab setup investigates
the described test setup with 1 GST link and 2 SM links (see also Figure 17). The network tester in
this setup is a Xena Networks Xenabay with M2SFT+T (10G LAN) line cards. A Xilinx VCU110 (Virtex
UltraScale XCVU190-2FLGC2104E) serves as hardware platform. The used FUSION IP has the version
number V1.5. The GST MTU of 16000 Bytes is chosen since this is the maximum value of the FUSION
IP and leads to worst-case values. The setup selects 9622 Bytes for the packet size for the SM links,
as this is the maximum size the network tester that was used can process; and which would also
cause worst-case values for latency and latency variation. Further, for investigating high load
conditions, a constant bitrate traffic pattern with 7864.32 Mbit/s (CPRI option 7 equivalent; 9830.4
Mbit/s without 8Bit/10Bit line coding -> 7864.32 Mbit/s) is used for the GST link as well as for the SM
links. The fibre connections of the lab test are shorter than 2m.
Figure 23 shows the measured results for latency and latency variation using an MTU of 16000 Byte
for GST and a packets size of 9622 Byte for SM links. Since the two results for SM1 and SM2 look
almost identical, the figure shows only the evaluation for SM1. The upper part of the figure showing
the GST results indicates a minimum latency of 14180 ns, and a maximum latency of 14316 ns
resulting in a maximum latency variation of 136 ns. The lower chart describing the SM results with a
minimum latency of 8769 ns and a maximum latency of 11177 ns, and introduces a latency variation
of 2408 ns. In contrast to the GST chart, the latency histogram for SM clearly shows the range of the
variance in the latency indicated by the red arrow.
Figure 23: FUSION: Measured results for latency and latency variation using an MTU of 16000 Byte for GST and a packet
size of 9622 Byte for SM
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4.4.1.4 Discussion
The comparison of the results of the theoretical estimation with the measured results leads to the
conclusion that the values for minimum latency and maximum latency reside in the same order of
magnitude. The fibre connections of the lab test are smaller than 2 m (per connection). Therefore,
the fibre delay is negligible (<10 ns) for the comparison for the absolute values, and has no effect on
the relative values for the latency variation. In the case of the latency variation for SM, this is almost
the same value. The latency and latency variation results are within the expected limits.
4.4.2 Time-aware-shaping (TAS) reference scenario
An example reference scenario for TAS use-cases is shown in Figure 24(a). The TAS is applied
towards the edge of the mobile network where the fronthaul networks are formed. The fronthaul is
made up by a pool of digital units (DUs) which are connected, through Ethernet, to remote units
(RUs) or remote radio head (RRHs). These distributed RAN entities can perform different split
functionalities, with some using a centralised processing (IQ radio transportation, DU to RRH pair)
while others use a split at the LTE MAC/PHY interface (DU to RU pair). Some of the RAN processing is
carried out in nodes that are closer to the core (for example implementing higher layer splits at the
PDCP/RLC interface). The DUs then perform the rest of the LTE processing up to (and including) the
LTE MAC layer. MAC/PHY split data flows are then transported to the RUs (which perform the PHY
layer processing) over the fronthaul links. At the same time, timing flows (e.g. PTP) are provided over
the fronthaul, with PTP boundary clocks (PTP BCs).
Local and global scheduling over the TAS application area is provided though scheduling entities
(these can be SDN-type controllers). The global scheduler communicates configuration parameters,
between the switch and the end-stations, regarding window section configurations.
Figure 24 (a) Reference architecture for the time-aware shaper use-cases presented in this work and (b) Scheduling design
concept.
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The IEEE 802.1Qbv standard offers a solution to tackling contention-induced FDV in a bridged
network. Traffic flows are assigned to different window sections (or sub-sections within). High
priority (HP) traffic is assigned to a protected window section (PS) or subsection (PSS) while low
priority (LP) traffic is assigned to a best effort section (BES) or subsection (BESS) within. Port gating is
applied such that a traffic flow is only allowed to pass through the switch in its allocated section. For
this scheme to work, an overlaid time synchronisation network is assumed present. The division of
the total transmission window (TW), encompassing all traffic sources, into the different sections is
shown in Figure 25. To prevent the best effort traffic from overrunning into the PS, a guard period
(GP) is used, where no transmission is allowed.
An example of high-priority traffic in the network is PTP traffic while control and management
(C&M) traffic will usually be treated at a lower priority setting. It is possible within the BES to assign
priority levels to the different lower priority streams and employ an “intra-section” scheduler such
as SP (strict priority), weighted-round-robin (WRR) or weighted fair queuing (WFQ).
Figure 25 Generic time window, window section and subsection plan based on IEEE 802.1Qbv
The baseline for the results presented here is the SP algorithm. With SP, the different queues
transmit in the order of priority setting. Thus, an LP queue has to wait for all the higher priority
queues to finish their transmissions before it is allowed to transmit. The network implementation in
OPNET is shown in Figure 26. It consists of two traffic generators (TGs); one of them representing
the PTP grandmaster (TG1) while the other (TG2) the best effort traffic generator. TG1 sends data
over VLAN ID 10 while TG2 sends data over VLAN ID 20 in a port-based configuration (i.e. the end
stations do not tag the frames).
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Figure 26 Network Scenario implemented in Opnet. All network interfaces are 1 GbE
In the first scenario, background traffic is generated as a burst of fifty frames, with an inter-frame
gap of 20 μs and a frame size of 1000 octets. This traffic source may represent either CPRI-type
traffic or C&M traffic. The PS duration is set to 50 µs.
Figure 27 shows the peak and average FDV results for SP and for TAS with different GPs. The results
show that the worst case TAS performance (zero GP) is equivalent to that of SP. This makes sense as
in both cases ongoing transmissions cannot be resolved. The step-like behaviour for the TAS results
is an effect of the resizing of the BES in order to accommodate the GP (i.e. the TW remains
constant). As the GP is increased, there is no change in FDV until the GP “eliminates” the frame from
the burst that is closer (in time) to the GP boundary. This can be seen by observing that the step
changes for the peak FDV occur at GP values, that when added to the corresponding FDVs, are
approximately equal to one background frame serialization.
As the GP is increased, both the average and max FDV with TAS reduce steadily until they reach zero
at a GP of 6 μs, which corresponds to a serialization of a large part (75%) of a background traffic
frame.
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Figure 27 Average and peak FDV for the PTP traffic with SP and TAS with different GPs. The background traffic source is
constant frame-rate and constant frame size
The second scenario is similar to the first, but with a varying frame size for the background traffic.
The traffic source is meant to represent functional split traffic, e.g. for fifty user allocations per LTE
subframe (i.e. 50 frames every 1 ms), in a MAC/PHY split (3GPP option 6). Note also that a constant
(or close to constant) number of allocations could arise as a result of employing statistical
multiplexing gains over a trunk link. Two different settings are used: The first follows a normal
distribution with a mean value of 1000 octets and variance of 200 octets (Figure 28). The second is
similar, albeit with an increased variance of 500 octets (Figure 29).
The results show that the peak and average FDV is increased (compared to the first scenario) for
both SP and TAS with zero GP, and approaches the serialization delay of a full background traffic
frame. Furthermore, the peak FDV for the results of Figure 29 reaches zero at a GP that is equivalent
to the serialization delay equivalent to that of a frame with a size equal to the average value of 1000
octets. This is indicative of the dependence of the scheduler performance, with regards to FDV, on
the transmission pattern characteristics of the traffic sources.
Figure 30 is a zoom-in of Figure 29 in the x-axis range from 0 to 1 μs. The small inset shows the
resulting time-stamping error with PTP for the peak FDV values, assuming that this peak FDV is
encountered in one direction of traffic (either downlink or uplink) while there is zero FDV in the
opposite direction.
This result shows the main limitation of SP which, although it can reduce significantly the average
FDV, the peak FDV remains constant and can potentially result in large PTP time-stamping errors
(depending on the size of the background traffic frame). TAS on the other hand looks promising in its
ability to reduce FDV (and thus time-stamping errors) as the GP is increased, or eliminate FDV
entirely when the GP is sufficient to eliminate contention. The drawback in this case is the increased
end-to-end latency, especially if the number of aggregation nodes becomes large.
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Figure 28 Average and peak FDV for the PTP traffic with SP and TAS with different GPs. The background traffic source is
constant frame-rate with a varying frame size following a normal distribution with mean of 1000 octets and variance of 200
octets
Figure 29 Average and peak FDV for the PTP traffic with SP and TAS with different GPs. The background traffic source is
constant frame-rate with a varying frame size following a normal distribution with mean of 1000 octets and variance of 500
octets
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Figure 30 Zoom-in in the region of GPs from 0 to 1 μs for the results of Figure 29. The inset shows the worst-case PTP time-
stamping error that would result from the peak FDV values
4.5 High-speed and low-cost transmission links
4.5.1 100 Gbit/s per wavelength
Low-cost requirements are leading to increasing considerations being given to using intensity
modulation and direct detection (IMDD) together with grey optics for 100G trunk lines. To make the
system as simple as possible, optical amplification and dispersion compensation are also not an
option. Consequently, the 1300nm transmission window is required to prevent severe limitations
from chromatic dispersion. For the modulation format, non-return to zero (NRZ) would be the
simplest solution; however this requires expensive high-bandwidth optics and electronics. Hence,
advanced modulation formats combined with DSP (digital signal processing) and FEC (forward error
correction)-encoding are considered here, with potential candidates being PAM-4 (Four-level pulse
amplitude modulation) and DMT (discrete multi-tone).
Figure 31 shows the employed experimental setup. Offline DSP is applied at the transmitter as well
as at the receiver side and requires the use of a high-resolution DAC (digital analogue converter) and
ADC (analogue digital converter). Both operate at a sampling speed of 84 GS/s, have a nominal bit
resolution of 8 bit, and show a 3-dB bandwidth of around 15 GHz and 18 GHz, respectively. At the
transmitter, the differential outputs of the DAC are first amplified by a linear, differential input and
single-ended output modulator driver (MAOM-003115) driving directly the succeeding electro-
absorption modulated laser (EML). The integrated high frequency coils of the driver allow control of
the bias of the EML and the driver. Furthermore, the gain of this driver is adjustable up to a
maximum of 9 dB, and delivers a maximum output swing of 2V. The 3-dB bandwidth of the driver is
around 25 GHz. The EML operates at a fixed temperature of 45°C, and the current of the distributed
feedback laser (DFB) section is set to a maximum of 100mA, which gives the highest linear range and
highest optical output power. At these operating conditions, the transmission wavelength of the
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Table 4: DMT System Parameters.
Modulation Formats BPSK to 64QAM
Frame Length (data
symbols) 128
Training Symbols (TS) 4
FFT Length 512
Usable carriers 255
Cyclic Prefix 1/64
Clipping Ratio 15dB
Equalizer 1-tap decision-directed
EML is around 1308nm and the 3-dB bandwidth is measured to be around 27 GHz, but with a
smooth roll-off. The optical link setup consists of conventional standard single-mode fibre (SSMF)
with an attenuation of around 0.32dB/km at 1300 nm, a variable optical attenuator (VOA) with an
integrated power monitor, and a PIN-photodetector (Picometrix PT-40E) integrated with a linear
trans-impedance amplifier (PIN/TIA) with a combined bandwidth of 35 GHz. Finally, the signal is sent
back to the ADC and stored for offline processing.
Figure 31: Experimental transmission setup for 100G.
Discrete Multi-Tone Transmission (DMT)
Discrete multi-tone transmission (DMT) as a special variant of orthogonal frequency division
multiplexing (OFDM) employs the properties of Hermitian symmetry and the IFFT (Inverse Fast
Fourier Transformation) to create a real-valued signal with the frequency spectrum divided into
orthogonal subcarriers. Each subcarrier can be modulated and the power of each subcarrier can be
allocated based on the water filling method. This process is known as bit and power loading (BL, PL)
and enables the effective compensation of channel impairments and component bandwidth
limitations without applying complex
signal processing, e.g. a simple 1-tap
equalizer at the receiver side is efficient.
To apply BL and PL, the transfer function
of the transmission system is first
estimated in terms of the signal-to-noise
ratio (SNR) at the receiver with 16-QAM
(quadrature amplitude modulation)
constellations with equal power on each
subcarrier. Afterwards, Chow's margin-
adaptive bit loading algorithm and
Cioffi's power loading are applied to efficiently distribute the bits and allocate the power. Figure
32b) shows the estimated SNR (signal noise ratio) of the transmission setup for the optical back-to-
back case, together with the corresponding bit and power allocation for a 112-Gb/s DMT signal.
From the estimated SNR, we can also estimate the available bandwidth of the transmission system:
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an SNR of 15 dB or more is available up to 25 GHz, while it drops below 0dBm for frequencies above
30 GHz. Figure 32a) illustrates the DSP blocks of the analysed DMT system and Table 3 summarizes
the most important DMT system parameters. To meet the previously mentioned memory
requirements of the DAC and the ADC, a DMT frame consists of 124 data symbols and four training
symbols (i.e. 128 DMT symbols in total), which are used for channel estimation and synchronization.
Figure 32: a) DSP blocks of the implemented DMT system and b) i) estimated SNR per subcarrier at the receiver optical back-
to-back and the applied ii) bit loading and iii) power loading
Figure 33 shows the achieved BER (bit error rate) vs. received optical power (ROP) into the PIN/TIA
for different data rates as well as for different transmission distances. Since DMT allows one to easily
switch between different data rates by loading a different number of bits onto each subcarrier, the
performance of 112 Gb/s, 89.4 Gb/s, 74.7 Gb/s, and 56 Gb/s is investigated and compared. Two
different FEC thresholds are added as a solid and a dashed line, representing the standardized KP4-
FEC (RS(544,514,10)) with a BER-limit of 2E-4 and the continuously-interleaved BCH FEC (CI-
BCH(1020,988)) with a BER-limit of 4.4E-3 [28][29]. Transmitting at a data rate of 112Gb/s, BERs
below the CI-BCH FEC are achieved only for the optical back-to-back (b2b) case, while BERs around
the FEC threshold are achieved in the case of a 10-km transmission distance. At a bias of 1.25V, the
output power of the deployed EML is around 1dBm, which results in a maximum achievable input
power of 5 dBm after 20 km transmission at this wavelength (0.32 dB/km*20 km).
Figure 33: Transmission results of DMT at different data rates and for different transmission distances.
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This input power is not sufficient to achieve BERs below the FEC-limits in case of 112 Gb/s. Indeed, a
very similar performance is demonstrated for the different transmission distances up to the
achievable input powers. The performance improves with decreasing bitrate.
PAM-4
Four-level pulse amplitude modulation (PAM-4) encodes 2-bits into one symbol, resulting in a four-
level signal and reducing the transmission bandwidth by a factor of two compared to on-off-keying.
Utilizing Nyquist pulse shaping with a small roll-off factor (β=0.1), the signal bandwidth can be
further reduced, resulting in an electrical bandwidth of around 30 GHz for a 112-Gb/s PAM-4
signal. Figure 34(a) shows the implemented offline DSP blocks for the Nyquist PAM-4 system. A 4-ary
deBruijn sequence of order eight (48=65536 symbols) is used and grey-mapped onto a PAM-4 signal.
Compared to DMT, PAM-4 offers the possibility of easily compensating the nonlinear transfer
function of the modulator by adjusting the levels towards equally spaced power levels after the
modulator. Afterwards, the signal is upsampled to 3 samples/symbol, undergoing raised cosine
shaping in the frequency domain with β=0.1, and downsampled by a factor of two, generating a
112 Gb/s Nyquist-PAM-4 signal with the 84GS/s DAC. Furthermore, digital pre-emphasis
compensates the bandwidth limitations of the DAC and driver, and the signal is quantized into
integer values between 0 and 255, in order to use the full 8-bit resolution of the DAC. Figure 34(c)
illustrates the obtained eye diagrams after the driver amplifier as well as after the EML, exhibiting
the typical over- and undershoots of a Nyquist PAM-4 signal. Furthermore, the power spectrum
density (PSD) of the transmit-signal before the DAC demonstrates the effect of pre-emphasis (grey
area = uncompensated signal (Figure 34(c)). At the receiver, the signal is resampled to 2-fold
oversampling, clock-recovery by means of the Gardner loop, and adaptive symbol-spaced feed-
forward equalization (FFE) is applied to recover the PAM-4 signal. The BER is calculated from the
detected and transmitted bits.
Figure 34: a) DSP blocks of the PAM-4 system, b) digital PSD of transmit signal and c) eye diagram after the EML.
The performance of the pre-equalizer (Tx-FFE) in combination with the applied FFE at the receiver
(Rx-FFE) is evaluated in terms of BER-performance as a first step. In principle, the question of how
many Tx-FFE and Rx-FFE coefficients are necessary for such a transmission scenario in order to
achieve BERs below the desired FEC threshold is answered. Figure 35(b) and (c) depict the BER vs.
ROP results for optical b2b, using 5 and 61 Tx-FFE coefficients, respectively, in combination with a
different number of applied Rx-FFE coefficients. The number after the term "FFE" notates the
number of used coefficients. Again, the previously discussed FEC thresholds are shown as black lines.
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Applying 5 Tx-FFE coefficients, up to 31 Rx-FFE coefficients, is necessary to achieve BERs below the
CI-BCH FEC-limit; while 11 Rx-equalizer coefficients are required when 61 Tx-FFE coefficients are
used. The interaction between the number of Tx-FFE and Rx-FFE coefficients is further illustrated as
a contour plot in Figure 35(d), where the achieved BERs for different Tx-FFE/Rx-FFE combinations at
a fixed ROP of 0dBm are shown. Basically, no significant BER improvement is seen with more than 11
Tx-FFE coefficients, while at the receiver at least 21 Rx-FFE coefficients are required. To achieve BERs
below 1E-3 however, more than 40 coefficients for both Tx-FFE and Rx-FFE are necessary.
Figure 35: Optical back-to-back transmission results of 112Gb/s PAM-4 employing different numbers of pre- and post-FFE
coefficients: a) shows the optical eye diagrams obtained directly after the EML using a pre-equalizer of 5 coefficients and 61
coefficients, b) and c) illustrate the BER vs. ROP results using different numbers of post-FFE coefficients and d) depicts the
BER performance for different Tx-FFE/Rx-FFE combinations at an input power of 0dBm.
Based on the results of Figure 35 Tx-FFE and 21 Rx-FFE coefficients offer a good trade-off between
performance and complexity and are used for further evaluation. With these settings, the
performance for optical b2b, 10 km, and 20 km is compared in Figure 36. For optical b2b and for 10-
km SSMF, the results stay well below the CI-BCH FEC threshold, however, the KP4-FEC threshold is
not reached. In addition, the limited output power of the EML prevents the possibility of
transmitting over 20 km. Up to the achievable input power a similar performance of the different
transmission distances is shown.
Figure 36: Transmission results of 112 Gb/s PR PAM-4: a) using different MLSE memory length after the FFE in case of
optical back-to-back transmission
Conclusion
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In summary, transmission over 10 km of SSMF was successfully demonstrated for both modulation
formats (DMT and PAM-4) if an HD-FEC of 4.4E-3 is assumed, allowing error free transmission. The
CI-BCH FEC is required for a transmission distance of 10km.
4.5.2 Beyond 100 Gbit/s per wavelength
Transmission rates appreciably beyond 100 Gbit/s per wavelength require the utilization of new
signal generation schemes, together with advanced modulation formats and digital signal
processing. This is especially the case if simple optical components are mandatory, i.e. intensity
modulation and direct detection schemes, to avoid higher costs [30][31]. In order to exploit the
available bandwidth offered by the latest optical components, high-speed digital-to-analogue
converters (DAC and ADC) with analogue bandwidths beyond 30 GHz are required. So far, low-cost
CMOS technology has only achieved bandwidths of around 25 GHz [32]. To overcome the bandwidth
bottleneck, two techniques have been proposed. The first one, spectral up-conversion [33], enables
140 GBd optical BPSK transmission, using electrical up-conversion of multiple sub-bands. In [34] a
combined discrete multi-tone (DMT) and orthogonal frequency division multiplexing (OFDM)
approach with independent sub-bands has achieved 178 Gbit/s. The second technique utilizes a
high-speed analogue multiplexer together with two DACs to generate a wideband signal.
Transmission rates of 214 Gbit/s using pulse amplitude modulation (PAM) or 300 Gbit/s using DMT
have also been shown [35][36].
In the work presented here, the feasibility of the first technique: the utilization of electrical up-
conversion to generate wideband signals, is investigated. The performance of spectral up-conversion
is demonstrated by utilizing a commercially available electrical IQ-mixer, together with single-carrier
(SC) modulation, like PAM and QAM and multi-carrier (MC) modulation like DMT and OFDM. In
addition, soft decision forward error correction (SD-FEC) is used as it enables error free transmission
at higher BERs, at the price of additional overhead [37]. The BER limit is then at 2.7x10-2, as opposed
to the standard hard-decision FEC (HD-FEC) where it is at 3.8x10-3. Altogether, we demonstrate a
record high transmission rate of 200 Gbit/s and 224 Gbit/s, for SC and MC modulation of two sub-
bands for spectral up-conversion in an optical back-to-back experiment.
A. Concept and Setup
Figure 37: System concept for spectral up-conversion utilizing IQ mixers and several independent sub-bands. a) Electrical
spectrum after DAC, b) after IQ-mixing, c) after signal combining, d) after optical modulation.
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The general system concept for spectral up-conversion is shown in Figure 37. Each DAC generates a
baseband signal, i.e. the in-phase (I) or quadrature (Q) of a complex signal. The signals are up-
converted onto the local oscillator (LO) frequency by means of an electrical IQ-mixer. After
combining multiple sub-signals, the compound wideband signal drives an optical intensity
modulator. After propagating along the fibre, a direct detection receiver performed optical-to-
electrical (O-E) conversion. The signal is split passively, filtered and the individual sub-bands are
down-converted. Finally, the resulting baseband signals are digitized with an array of ADCs.
Figure 38: Experimental setup for spectral up-conversion based IM/DD transmission links.
Figure 38 shows the experimental setup of the proposed multi-band architecture. Three channels of
an arbitrary waveform generator (AWG), working at 80 GS/s, were used to generate two
independent signal bands: a baseband signal at 0-21 GHz and an up-converted signal at 21-39 GHz.
The up-converted signal was formed with the help of an electrical IQ-mixer, driven by two baseband
signals and a local oscillator (LO) at 30 GHz. The baseband signal and the up-converted signal were
set to a fixed power ratio and combined using a diplexer.
The amplified electrical signal drives a Mach-Zehnder modulator (MZM). The MZM was biased at the
quadrature point to operate as an intensity modulator with an average output power of 4 dBm. The
laser was a distributed feedback laser (DFB) at 1550.5 nm. After optical modulation, the signal was
transmitted over different standard single mode fibre (SSMF) lengths. An optical filter was applied
after the MZM to enable a single sideband (SSB) transmission at fibre lengths beyond 2 km. This was
necessary to reduce the influence of the chromatic dispersion (CD). The receiver consisted of a
wideband photodiode (PD 45 GHz) with an additional optical amplifier (EDFA). After O-E conversion,
the signal was amplified and split by a passive coupler. One signal was low-pass filtered and
recorded by a digital storage oscilloscope (DSO), whereas the second signal was down-converted by
an IQ-mixer, filtered and recorded likewise. The DSO had a sampling rate of 80 GS/s at 30 GHz
bandwidth. All digital signal processing (DSP) was done offline.
For the transmission experiments two different scenarios were considered. In the first scenario the
baseband signal was PAM modulated at a fixed rate of 40 GBd and the up-converted signal was QAM
modulated at a fixed rate of 16 GBd. The DSP for PAM and QAM consisted of pre-equalization to
compensate for the DAC frequency roll-off, a root-raised cosine filter at the Tx and Rx (roll-off 0.1),
and an additional linear finite impulse response (FIR) based equalizer at the receiver, to compensate
for the remaining system frequency response.
The same bandwidth assignment for baseband and up-converted signals has also been used for the
second scenario. Here, a DMT signal was used in the baseband and an up-converted QFDM signal in
the upper band. Both consisted of 256 subcarriers. The modulation order of each subcarrier was
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adapted to the channel properties by SNR estimation of training symbols. At the receiver, channel
estimation and correction were performed for every subcarrier.
For the upper signal band with QAM or OFDM modulation, an additional IQ-imbalance correction
was performed, in two steps prior to equalization. First, a 2x2 multiple-input multiple-output
(MIMO) time domain correction of the signal (I, Q) was applied, eliminating the phase difference of
the Tx and Rx Los, and compensating amplitude variations of the signals relative to each other.
Second, a 2x2 MIMO correction of each frequency with respect to the mirror frequency, compared
to the LO, was applied. This was necessary to compensate for frequency-dependent IQ-imbalance of
the mixers, which results in a crosstalk between mirroring frequencies.
B. Results
First, we evaluated the performance of the single carrier approach, i.e. scenario #1 with PAM and
QAM. Figure 39 (a+b) shows the measured BERs for 40 GBd of 4/8-PAM in the lower band and
16 GBd of 8/16/32-QAM in the upper band for different lengths of optical fibre. The following can be
observed for the lower band at 0-21 GHz: 4-PAM is possible for back-to-back (btb), 2 and 10 km
reaches using a HD-FEC; 8-PAM permits only btb transmission using a SD-FEC. For the upper band at
21-39 GHz the observations are: 8-QAM is possible at all fibre lengths using HD-FEC; 16-QAM at btb
and 2 km using HD-FEC; and 32-QAM allows only btb transmission using SD-FEC.
Figure 39 (a+b) Measured BERs for PAM/QAM modulation at different fibre lengths (scenario #1). (c+d) Measured data
rates for different target BERs using DMT/OFDM (scenario #2).
The total transmission rates for scenario #1 below the HD-FEC limit are thereby 144, 144, 128 and
88 Gbit/s for btb, 2, 10 and 20 km of SSMF, respectively. For the SD-FEC limit and btb, 2, 10 and
20 km of SSMF, data rates of 200, 160, 144 and 144 Gbit/s were measured, respectively.
For the MC based approach, i.e. scenario #2, the performance of the DMT and the OFDM signal was
determined for different target BERs and fibre lengths as well. Figure 39 (c) shows the results for the
lower band, i.e. the DMT signal and Figure 39 (d) for the upper band, i.e. the OFDM signal. At the
HD-FEC BER limit a total transmission rate, i.e. the sum of the DMT and OFDM data rates, of 179,
171, 141 and 134 Gbit/s for 0, 2, 10 and 20 km of SSMF respectively, can be determined. For the SD-
FEC case 224, 219, 175 and 167 Gbit/s can be achieved. The relatively strong degradation of the BERs
in both scenarios at fibre lengths of 10 km and 20 km can be primarily attributed to the finite slope
of the optical SSB filter.
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Figure 40: (a+b). Measured BERs for PAM and DMT at 80 and 120 Gbit/s for the lower band at different fibre lengths. (c+d)
Measured BERs for QAM and OFDM at 64 and 80 Gbit/s for the upper band at different fibre lengths.
In order to compare the SC and MC approach, we determined which BERs can be achieved for
DMT/OFDM, at the data-rates of the PAM/QAM transmission. This was done by interpolating the
curves of Figure 39 (c+d). Figure 40 (a) shows the results for 80 Gbit/s, which relates to 40 GBd 4-
PAM in the lower band. It can be seen that the determined BERs of the DMT signal are slightly
worse. A similar behaviour can be observed for 8-PAM in Figure 40 (b). These findings are in
accordance with the theoretical considerations, since PAM offers a better performance in terms of
SNR in a system with a relative flat channel, due to the much better peak-to-average-power ratio.
For the upper band, consisting of the QAM or the OFDM signal, a different behaviour can be
observed. Figure 40 (c) shows the BERs for a data rate of 64 Gbit/s, which relates to the 16 GBd of
16-QAM. Here, the performance of the OFDM modulated signal is clearly better. For 16 GBd of 32-
QAM, a similar examination can be made as shown in Figure 40 (d).
The superior performance of the multi-carrier based approach in the upper band can be attributed
to several effects. First, the total system frequency response with a 3-dB bandwidth at around
32 GHz, suits the OFDM signal much better due to the easy adaption in terms of subcarrier bit-
loading. For QAM, the drop in the frequency response results in a noise enhancement due to the
necessary equalizer at the Rx. Secondly, the characteristics of the electrical IQ-mixer at the Tx and
the Rx causes further degradations. Primarily, these are penalties due to the frequency-dependent
IQ-imbalance and port isolation. Both penalties can also be much better addressed with the
subcarrier-based structure of OFDM, which results in lower penalties as compared to QAM.
C. Summary
The proposed electrical up-conversion based technique enables the generation of signals with
bandwidths beyond the state-of-the-art systems. Further, due to the use of mature components,
generally lower costs as compared to a purely optical solution can be achieved. The investigation of
SC and MC modulation formats for the presented concepts showed a better performance for the SC
approach in the lower band (baseband) and a better performance of the MC approach in the upper
band (up-converted band). This can be primarily attributed to the different channel characteristics in
both bands. The achieved transmission rates at the SD-FEC limit are 200 Gbit/s and 224 Gbit/s for
the SC and MC approach, respectively.
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4.5.3 Wireless transmission over millimetre wave
The iCIRRUS project foresees an ever-increasing use of wireless (including both mmW and optical
wireless communications) to support both fixed and mobile applications [38][39] leading to the use
of interlinked small cell architectures [40][41][42], as a means to achieving high bandwidth and low-
cost transmission. The bi-directional nature of this traffic will require that these small cell network
elements will need to be capable of supporting backhaul as well as fronthaul applications in order to
satisfy many of the likely applications. As a result, the demands on antenna designs have become
extreme. To some extent, short to medium range meshed topologies can negate the necessity for
very high gain antenna systems of the type seen in point-to-point links, but any device must provide
service up to a nominal distance of around 100 metres to be useful. Also the requirement for
omnidirectional signal capture and a very wide bandwidth remains, if the antenna is to support high
data rate, multi-user applications. Solutions to this issue mostly include the use of multi-element
antenna arrays that rely on the use of a great deal of signal processing to implement complex beam
steering, beam forming and MIMO techniques. The device described here embraces a different
approach in that it is inherently capable of operating over a 360 degree capture range whilst
maintaining a gain of 13dBi at 57 GHz, which is then maintained at over 12 dBi at 64 GHz, the upper
limit of the IEEE802.11ad frequency allocation. Uniquely, this has been accomplished by treating the
circular radiator as a horn antenna and concentrating the resulting radiation horizontally to form a
flattened disk-like radiation pattern with a full-width half-maximum (FWHM), far field, frequency
dependent divergence angle of between 6.5 and 8.2 degrees. Unlike many multi-element designs
this radiation pattern is inherently stable across the IEEE802.11ad frequency band, and so
unaffected by phase-related beam anomalies (squint). This work represents further development
and resulting practical embodiment of the device recently described in [43]. The unique capability of
this device is its suitability to be deployed in a mesh network communicating with a number of user
scenarios simultaneously. Coupled with its high gain properties that enable a potential 100 metre
operating range, this device represents a useful bridge between the backhaul, mesh cell, mobile and
fixed users of mmW wireless systems as exemplified by the iCIRRUS architecture.
Figure 41: Antenna model graphic showing conical top and bottom sections with integral matching rings, antenna feeding
arrangements and modified support pillars, with surface current plot showing dissipation effect of the matching rings.
D. Antenna Model Structure / Design Philosophy
In order to accomplish the omnidirectional and wide bandwidth requirements, features related to a
bi-conical design have been adopted. A high gain performance was achieved by treating the device
as a horn structure, so using a monopole feed rather than the dipole-like feeding arrangement
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normally associated with conventional bi-conical designs [44][45][46]. Here it can be seen that two
opposing metallic conical structures provide the basic propagating mechanism. An integral part of
these are the matching rings shown on the upper and lower outer aspects of the conical sections
[47][48][49]. These rings also provide convenient anchoring points for the Perspex (Ɛr 3.4) support
pillars used to separate the two cones, with these being spaced away from the main body of the
structure as well as being reduced in thickness to 3mm to minimize the predicted “shadowing”
effect of the pillars on the radiation pattern. Having dimensions of 5 mm x 10 mm, the matching
rings provide a predicted increase in gain of between 1.5 dB and 2.2 dB across the 57-GHz to 64-GHz
range [43]. As can be seen in Figure 41 this was achieved by the effective dissipation of disruptive
surface currents traveling backwards across the outer face of the structure.
The horn feed is formed from a section of Huber and Suhner SR 86 semi rigid coaxial cable with a
section of the central conductor exposed to form a central monopole radiator. It was determined
that the optimal length for the monopole launch was 1.15 mm. This was based on a dimension that
gave a maximum gain / bandwidth product. This feeding arrangement was positioned at the centre
of the lower conical structure around which is a flat section that, in conjunction with the reciprocal
arrangement in the top cone formed a symmetrical quarter wavelength waveguide launch. With a
central operating frequency of around 60 GHz this central flat section was assigned a diameter of 2.5
mm so as to allow for a quarter wavelength dimension in all directions. The height of the waveguide
section was initially taken from that of WR15 waveguide as 1.88 mm. These dimensions allow for an
excellent broadband performance, good impedance match and desirable propagation
characteristics. Finally, dimensions of the conical disk section were determined as having a radius of
75 mm (15 wavelengths at 60 GHz) with an empirically determined critical mouth dimension of 21.94
mm, so giving a 15.5 degree flare angle after the waveguide section.
Figure 42: Antenna assembly, showing all component parts and the support pillars and probe clamping arrangement.
As can be seen in Figure 42, the body of the new device was fabricated in aluminium, with the
modified Perspex support pillars and feed probe clamp also clearly visible. A commercially available
MMPX connectorized semi-rigid cable was used for the feed probe, with a specified -16dB return
loss. The feed and clamp arrangement was positioned in a closely fitting centrally drilled hole in one
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of the aluminium conical sections so as to ensure contact with the outer casing of the semi-rigid co-
ax. Using an Anritsu 37397D Vector Network Analyser (VNA) this was then carefully positioned so as
to give the best S11 measurements. The newly developed clamping arrangement then locked the co-
ax this in position.
E. Antenna Performance Measurements
With the VNA calibrated across a 30-GHz to 67-GHz range, measurements were conducted with
Figure 43 showing the resulting S11 return loss plot with markers at the same frequencies as for the
simulation [6]. Overall this indicated good agreement with the modelled result across the frequency
range. With return losses ranging from -16.8 dB at 64 GHz falling to -28.9 dB at 60 GHz good coupling
across the frequency range was expected. Corresponding input impedance measurements were
taken as shown in the Smith chart in Figure 44. This shows a well-matched device with similar
properties to those previously predicted. The impedances at the four spot frequencies are closely
grouped around the ideal 50 Ω point; the only deviation being at 61 GHz that gave a measurement of
60.5 Ω.
Figure 43: Measured S11 return loss over the IEEE 802.11ad frequency range with markers at four spot frequencies.
Figure 44: Corresponding Smith chart showing measured antenna feed impedance values at four spot frequencies
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The real life gain and polarization characteristics of the antenna have also been assessed. Using the
Anritsu 37397D VNA and two Steatite QSH-SL-50-75-V-20 linearly polarized 20-dB gain horn
antennas set up at a distance of 1 metre, a reference channel was established. Although in a non-
anechoic environment this showed an essentially flat channel up to 65 GHz with a low-end -3 dB
point at 42.75 GHz with a steep roll off thereafter. One of the reference antennas was then removed
and substituted with the antenna to be evaluated. As can be seen in the co-polar trace in Figure 44
the antenna under test mimicked the frequency response of the reference horn antenna. Evident is
the low-end roll-off being the same as for the reference antenna used as the other control
(reference) antenna. It can also be seen that the gain difference of around 5 dB to that of the horn is
commensurate with that expected. The amplitude of the received signals was then measured at the
four frequencies shown in Table 5 and the antenna gains calculated taking into account the free
space path loss (FSPL), horn antenna gain and connector losses due to the MMPX socket. It can be
seen that apart from a gain measurement that is often around 1 dB higher than that predicted, there
is again good agreement with the predicted gains at all four spot frequencies; this being maintained
around the device periphery. Maximum gain variations of 0.43 dB at 57 GHz, 0.83 dB at 60 GHz, 0.69
dB at 61 GHz and 0.58 dB at 64 GHz were recorded. With this established in conjunction with the
other validation evidence gathered, the predicted radiation patterns appear to be credible.
Table 5: Comparison of Peripheral Measured Antenna Gain
As discussed earlier, this device was mostly aimed towards high capacity wireless mesh network
applications. In such situations a polarization diversity function may also be desirable for functions
that require frequency reuse. With this in mind, measurements were conducted to ascertain the
polarization isolation characteristics of the antenna. Again, using the same measurement setup the
linearly polarized horn antenna was rotated through 90 degrees and a plot was taken of the received
signal levels. This can be seen in the bottom trace in Figure 45. This determined that polarization
isolation ranges from 25.15 dB at 64 GHz rising to 29.14 dB at 57 GHz, which is adequate for most
applications. Also apparent from Figure 45 is the lower cut-off frequency limit measured, which is
that of the test horn antenna, while the upper limit is that of the VNA.
Frequency Reference
0 degrees
90 degrees
180 degrees
270 degrees
57 GHz 13.24 dBi 13.18 dBi 12.81 dBi 13.12 dBi
60 GHz 13.51 dBi 13.99 dBi 13.16 dBi 13.26 dBi
61 GHz 13.69 dBi 13.60 dBi 13.43 dBi 13.00 dBi
64 GHz 12.62 dBi 12.35 dBi 12.82 dBi 12.24 dBi
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Figure 45: Comparison of co-polar and cross-polar transmission characteristics between 30 GHz and 65 GHz, also illustrating
the wide bandwidth capability of the omnidirectional antenna.
C. Proof of Concept
To ascertain the novel omnidirectional aspects of this design a four-channel transmission system was
implemented using a variety of antenna types and frequencies simultaneously. This was designed to
represent the operation of the device in a mesh network setting where both fixed and mobile users
could be accommodated. These were positioned to cover 360-degree operation around the centrally
positioned omnidirectional antenna, which was connected to a spectrum analyser. The received
signals were captured as shown in Figure 46. Channels 1 & 2 represents a wide bandwidth user
requirement such as that of backhaul usage and were served by horn antennas. Channel 3
represents a mobile user and used a small coaxial slot array antenna, and channel 4 used another
identical omnidirectional antenna and so represents the mesh deployment.
Figure 46: Showing four channel capability over an operating angle of 360 degrees, demonstrating backhaul, mobile, and
mesh deployment usages.
D. Summary
The proposed linearly polarized omnidirectional antenna design described here operates without
the need for beam steering and possesses a gain in excess of 12 dBi gain over a frequency range
between 57 GHz and 64 GHz. A set of antenna measurements was additionally carried out to
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demonstrate that the radiation patterns are also reliable. The device discussed here has been shown
to possess novel high gain, and omnidirectional properties that will enable its deployment in a mesh
femtocell environment.
4.6 Test and performance monitoring This deliverable develops aspects of the “SLA and SON Concept for iCIRRUS” set out in deliverable
D3.3 towards practical experimentation in WP5, taking into account the constraints and
opportunities likely to be encountered in a putative real-world deployment.
The goal of SON in iCIRRUS, whether the SON objective is configuration, live improvement or
healing, is the creation of solutions that jointly account for the constraints of the fronthaul network
and the radio access network (RAN).
Changes or adaptation to the constraints in both fronthaul and RAN domains that delimit the
configuration space for optimisation are characterised by different timescales. Examples of changes
that occur on a slow timescale of the order of months to years include: deployment of new optical
fibre, deployment of new base stations, and acquisition of new radio spectrum. In these cases the
slow speed of change is due to the need for manual intervention and potentially even regulation.
Whereas, other changes may occur on a timescale of seconds to hours: for example, modification to
QoS parameters on a link, changes to transport path routing, or change to the maximum permitted
transmit power of a base station.
Consequently, in some scenarios, the test and performance monitoring related to a constraint may
be represented by semi-static measurements, appropriate for storing in a database of
configurations, whereas another constraint may be represented by “real-time” measurements.
The following Table summarises the timescales over which exemplary principal elements of the
fronthaul network may change, and the type of measurements that may be performed on a one-off
basis to characterise the property of that element, and may be stored in a database; and those
measurements that that are required to be conducted in an on-going manner to characterise the
real-time performance or failure of that element.
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Table 6: Characteristics of measurements in fronthaul domain
Measurement
object
Configuration
space
Changes Timescale Slow / one-off
measurements
On-going
measurements
Topology of
physical access
network
ducts, fibres,
splitters, MMW
links, copper
add fibres / add
PON technology/
change optical
split / change OLT-
OLU devices
Add MMW links…
Add copper links …
Months-
years
• Validate
inventory
• Discover
topology
• Check
connectivity
• (Audit 3rd
party
provider)
• Detect failure
(alarm)
Logical access
network
Link and
redundancy
routing
Change routing Seconds-
months
• Check
connectivity
• Discover
routing
• Detect failure
(alarm)
Logical access
network
Resource
allocation / QoS
Change resource
allocation
Milli-
seconds (eg
NGPON2)
-months (eg
3rd party)
• Service
activation
• Real-time
performance
measurement
The following Table performs the same task as that above, but for the exemplary principle elements
of the RAN network.
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Table 7: Characteristics of measurements in RAN domain
Measurement
object
Configuration
space
Changes Timescale Slow / one-off
measurements
On-going
measurements
Set of RAN
elements
Base stations &
antenna
systems,
ducts, fibres,
splitters, MMW
links, copper
Add base stations,
Add antenna
systems (support
MIMO, DAS)
Months-
years
• Validate
inventory
• Discover
geolocation
• Check
connectivity
• (Audit 3rd
party
provider)
• Detect failure
(alarm)
Cell level time
configuration
Time/ frequency
alignment
Frequency
synchronisation,
time of day
alignment
Months-
years
• Check synch
operation
• Detect out of
limit operation
(alarm)
Cell level
mechanical
configuration
Antenna height,
antenna
direction,
Change antenna
height, tilt,
antenna azimuth,
Days-
months
• Audit settings
• Check
connectivity (if
appropriate)
• Detect effect
of changes on
RF
performance
(RF level,
quality,
dropped call
etc)
Cell level
parameter
configuration
Base station
activation,
Transmit power,
inter-cell
synchronisation
Activate/
deactivate base
stations / small
cells, change max
transmit power,
change control
channel power
Minutes-
days
• Audit settings • Detect effect
of changes on
RF
• Detect failure
(alarm)
Inter-cell
parameter
configuration
Permitted CoMP
set size
Number of cells
allowed in CoMP
set
Minutes-
days
• Inter-cell
transport/
fronthaul
bandwidth
• Detect CoMP
performance
benefit
• Monitor CoMP
bandwidth
requirement
• Detect out of
bandwidth
(alarm)
The joint configuration space resulting from the allowed changes to the fronthaul and to the RAN on
a variety of different timescales gives rise to a large number of SON scenarios that may be exploited.
These are reviewed and summarised in the Section 5.2 example use-cases, that may be investigated
in WP5.
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5 Further architectural considerations
5.1 Evolved digital fronthaul vs. analogue fronthaul The evolved digital fronthaul concept investigated in D3.1 [50] and D3.2 [51] has been evaluated in
simulations [52] and experiments [53] against an analogue approach that promises similar
opportunity for reduction of the optical bandwidth on the fronthaul. In this section, the
experimental results are presented.
5.1.1 Reference system
The reference system for the experiments is a custom real-time 5 Gbit/s millimeter-wave (mm-wave)
transceiver as described in [54]. This system concept provides the background for a 10 Gbit/s
system; however not in real-time. The data rate of 10 Gbit/s is chosen with respect to 10 Gigabit
Ethernet, which is widely used in access networks and also considered for realization of the evolved
digital fronthaul [50].
The extended mm-wave system transports four channels at a data rate of 2.5 Gbit/s each. A π/4-
shift DQPSK (π/4-DQPSK) modulation is used with a symbol rate of 1.25 GBd per channel. To
minimize bandwidth usage, a channel spacing of 1.5 GHz together with pulse shaping and bandpass
filtering is applied. The digital signal processing (DSP) at the transmitter (Tx) and receiver (Rx) is
realized in Matlab. The following steps are taken in the Tx separately for each channel: first the user
data is encoded for forward error correction (FEC), then modulated using the π/4-SDQPSK
modulation scheme, and filtered using a root-raised cosine (RRC) filter with a roll-off factor of 0.25
for pulse shaping. In this step, the sampling rate of the baseband signals is increased to 2.5 GS/s.
At the Rx, a frequency domain equalizer is applied to each baseband channel along with another RRC
filter. In this step, the oversampling is removed, so that the sampling rate is 1.25 GS/s again.
Afterwards, previously to demodulation and FEC, the error vector magnitude (EVM) of the π/4-
SDQPSK symbols is estimated.
In this reference system both fronthaul concepts are integrated between the π/4-SDQPSK
transmitter and receiver, and are experimentally evaluated.
5.1.2 Analogue Fronthaul
The analogue fronthaul is modelled by up-converting the four analogue baseband signals of the
reference system to separate intermediate frequencies (IFs) and transmitting them on a common
optical carrier as shown in Figure 47. After baseband processing and oversampling to 20 GS/s, each
Figure 47: Signal processing for analogue fronthaul
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baseband signal is up-converted to its respective IF, and then added onto a common signal vector. A
random time offset is also added to every baseband signal in this step in order to avoid constructive
interference between the training sequences used for synchronization. The IFs used are 1.25 GHz,
2.75 GHz, 4.25 GHz and 5.75 GHz for the four channels. The resulting signal vector is exported and
digital-analogue converted with an arbitrary waveform generator (AWG). After transmission through
the optical fronthaul link (please refer to section 2.3), the signal is recorded with a real-time
oscilloscope at 80 GS/s. The Rx DSP consisted of band pass filtering, to separate the four channels,
down-conversion to baseband and RRC filtering. Afterwards, the signals are further processed as
specified for the reference system.
5.1.3 Evolved Digital Fronthaul
The signal processing for the digital fronthaul is shown in Figure 49. Compared to the analogue
approach, here the fronthaul Tx is located after the FEC encoding. The FEC-encoded data from all
channels is serialized as one data vector and OOK NRZ modulated at 10 Gbit/s. Afterwards the signal
is up-sampled to 20 GS/s and a training sequence for the Rx equalizer is inserted. After digital-
analogue conversion with the AWG, the signal was transmitted over the optical link (please refer to
section 2.3). At the Rx side, a simple finite impulse response (FIR) equalizer is applied, before down-
sampling to the symbol rate. After hard decision, the FEC encoded user data is recovered. To
evaluate the link performance, it is sufficient to determine the BER after the decider (dashed line),
since any accumulated errors are evenly distributed on the four wireless channels in the subsequent
signal processing.
5.1.4 Optical Link
The fronthaul signals are transported over an intensity modulation / direct detection (IM/DD) link.
Figure 48 shows a block diagram of the setup: the beam from a DFB laser with a wavelength of
1550.52 nm is first passed through a polarization filter and then modulated using a Mach-Zehnder
modulator (MZM). The MZM is driven using the output signal from the AWG, which is previously
Figure 49: Signal processing for evolved digital fronthaul
Figure 48: Experimental setup (optical link)
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low-pass filtered and passed through an amplifier. The MZM is biased at the quadrature point to act
as a simple intensity modulator. The launch power of the signal was around 2 dBm. After the MZM
the signal is transmitted over various lengths of SSMF. Afterwards an Erbium doped fibre amplifier
(EDFA) with a constant output power was used to achieve a sufficient signal level at the Rx. To vary
the optical power, an additional variable optical attenuator (VOA) was applied in front of the Rx. The
Rx consisted of a PIN photodiode with an integrated trans-impedance amplifier (TIA). After electrical
amplification, the signal was recorded using a real time oscilloscope at 80 GS/s.
The fibre used in the experiments had a dispersion coefficient of ~17 ps/(nm∙km) and an attenuation
of ~0.2 dB/km. Three patches of 25.5 km length each were used to achieve total fibre lengths of
25.5, 51.0, and 76.5 km. For a fronthaul scenario in the access network, 25.5 km are considered as
sufficient. The higher distances of 51.0 and 76.5 km address, e.g. large scale DU pooling and
fronthaul transmission in the metro network.
5.1.5 Results
In the following, the experimental results for both fronthaul solutions are presented.
Figure 50 shows the EVM over the received power for optical back-to-back (btb), 25.5, 51.0, and
76.5 km of SSMF. The solid lines depict the average EVMs over all four channels, the dashed lines
represent the maximum EVMs observed at any channel.
It can be seen that the penalty of 25.5 km fibre compared to btb is relatively low for higher received
power, and virtually inexistent at lower power. However, even at 0 dBm received power, an EVM
floor of around -22 dB for btb and -20 dB for 25.5 km occurs, due to general system limitations.
While this is not problematic for the radio link considered here to operate at low SNR, it is difficult
for the analogue fronthaul when the radio link is operated at high spectral efficiency. After 51 km
and 76 km of fibre, a clear impact of the CD can be seen. At 0 dBm received power the average EVM
is -16 dB after 51 km, and -12 dB after 76 km. More notably, the maximum EVM increases drastically
with distance: it is at -14 dB for 51 km, and at -7 dB for 76 km. As a reference, the EVM limit stated
for QPSK by the LTE standard is 17.5%, or around -15 dB [55], which is only fulfilled on all channels
for btb and 25.5 km, at a received power of at least -8 dBm.
EVM increasing with fibre length can be explained by the effects of chromatic dispersion, which
accumulates over fibre distance and causes signal fading at certain frequency ranges, starting at high
frequencies and moving to lower frequencies with increasing distance [56].
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Figure 50: Estimated EVM for analogue fronthaul over Rx power and for different fibre lengths
Figure 51: Estimated BER for analogue fronthaul over Rx
power and for different fibre lengths
Figure 52: Estimated BER for digital fronthaul over Rx
power and for different fibre lengths
This also explains the discrepancy between average and maximum EVM in the analogue system
towards longer distances: since dispersion induced fading is frequency selective, it only affects some
of the channels at a time.
Figure 52 shows the BERs associated with the fronthaul transmission, estimated from the EVM
values of each channel and then averaged over all channels. For 25.5 km of fibre, only minor
penalties compared to btb exist, even at very low BERs. In detail this is 1 dB (-7 vs -6 dBm) at a BER
of 10-12. With 51 km fibre, the estimated BER is at least ~10-7 for all power levels; and with 76 km
always above 10-3. Below a BER of 10-4 all errors can be corrected by the applied FEC algorithm
(Reed-Solomon 255/239). This condition is fulfilled for a received power of at least -10 dBm for btb
and 25.5 km, of -8 dBm at 51 km, and not at all at 76 km.
Figure 51 shows the BER of the received NRZ signal on the digital fronthaul over received power for
the different fibre lengths. For btb and 25.5 km, the performance is similar and unproblematic for
sufficient received power: at -10 dBm, the BER is below 10-12 for both distances. At 51 km fibre
length, the BER ranges between 10-6 and 10-5 for powers greater than -9 dBm. An estimated BER of
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below 10-4 is achieved for a received power greater than -14 dBm for btb and 25.5 km, and -12 dBm
for 51 km. Again, transmission over 76 km produces errors for all received power levels.
5.1.6 Comparison
Both fronthaul solutions perform reasonably well over distances of up to 25.5 km for sufficiently
high received power levels. Regarding the fronthaul alone, an acceptable error rate can be achieved
for a higher received power at 51 km, too; however, this distance appears to be the limit of the
system in both cases.
The necessary Rx power below the BER threshold of 10-4 is clearly lower for the digital fronthaul as
compared to the analogue fronthaul. In detail, this is for digital vs. analogue: -15 vs. -11.5 dBm for
btb; -14.5 vs. -11 dBm for 25 km, and -12.5 vs. -8.5 dBm at 51 km. This can be explained by the more
robust modulation format of OOK compared to π/4-SDQPSK. Especially, the far better peak-to-
average power ratio (PAPR) of OOK compared to analogue signals results in a significant gain.
For the distance of 51 km, the BER at high Rx power levels is better for the analogue fronthaul. This
can be explained by the smaller bandwidth of the analogue fronthaul signal as compared to the
digital signal, which results in smaller penalties due to the CD. At 76.5 km, both fronthaul concepts
experience severe penalties due to the CD, so only minor BER difference can be observed at high
power levels.
5.1.7 Conclusions
The findings outlined above show that despite the larger signal bandwidth, the NRZ-based digital
solution is more resilient, especially at the low received powers that might occur in a low-cost
system without optical amplifiers on the Rx side. The analogue fronthaul significantly affects the
transported baseband signal, especially for low received power levels. Both solutions, however,
show little dependency on fibre length regarding dispersion effects, at least up to 25.5 km distance.
In metro networks with covered distances of up to 100 km, the tested solutions do not hold up. Both
reach their limitations at about 50 km distance.
5.2 SON use cases As described in [57], SON includes families of solutions that address self-configuration, self-
optimisation and self-healing. The deliverable presented a summary of SON use cases and provided
a table that listed some exemplary use-case experiments to be used as a tool to help design use-case
experiments for the testbed phase of the iCIRRUS project in WP5. In this Section, we examine more
of the detail of the SON experiments that may be conducted the testbeds, considering the greater
knowledge that is available on the physical constraints of the testbeds, and also sources of data that
are expected to be available which may be analysed to construct a “real-world” background scenario
for the SON experiments.
There are certain prerequisites for experiments in the SON use-cases, including for example, that the
system exhibits stable operation in a static non-impaired environment. Further, that impairments
can be added into the radio and fronthaul domains in a controlled manner, and that the testbed
environment itself can be “placed” in the context of a more realistic network deployment. Then,
with a knowledge of the fronthaul topology and the possible RAN modes, the system determines
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configuration options. Whether a configuration is changed dynamically depends on which type of
SON solution is under investigation. Additionally, depending on the capability of the testbed, a
dynamic reconfiguration can be emulated piecewise as a set of static configurations.
A further input that is required to determine an optimal network configuration (and as a trigger to
reconfiguration) is a knowledge of the variation of the traffic demand across the network. The
extent to which traffic demand is time varying and unevenly distributed across the cells of the
networks affords opportunities
5.2.1 Representation of the radio environment
The mobile radio environment is characterised by sets of wanted and interfering signals that
experience time varying multipath propagation. In contrast, the testbeds in iCIRRUS have few
mobile devices and fewer cells.
The simplest way to approximate radio channel impairment in a controlled measurable manner is to
use a variable attenuator to statically attenuate a wanted signal. Interference may be represented
by the noise floor, or an interfering signal maybe injected. An alternative possibility is to set a lower
power level in the base station and mobile devices using configurable software parameters.
More accurate emulation may be achieved by use of a radio channel fader that creates multi-path
interference; the approach can be extended for MIMO and CoMP channel emulation. However, the
cost and complexity of a radio channel fader is outside of the iCIRRUS scope, even for the simple
case.
Additionally, radio channel measurement data from real networks can be analysed to approximate a
realistic radio “context” in which to operate the testbed. This would include, for example, a set of
signal power and quality values representative of operation across a cell. Placing the measurements
taken from the testbeds in a context representative of a real-world deployment allows the testbed
results to be extrapolated to estimate real-world performance. Adjustment to the performance to
represent degradation in fading channels (rather than static channels) is also required, which can be
approximated based on published data.
5.2.2 Representation of the fronthaul environment
The fronthaul network in a real network deployment is characterised by a set of possible routes over
physical infrastructure, which may include optical fibre, for example, as point-to-point links or using
optical splitters for multi-point transmission, copper, and millimetre-wave radio links. The
consequent fronthaul topology and the reconfiguration and redundancy opportunities define the set
of paths that may be configured and reconfigured. Each link over a path through the network may
be characterised by a set of KPIs as discussed in earlier deliverables, including: packet delay, delay
variation, packet loss, and from time to time by link outage. The traffic flows on the links may be
associated with Class of Service parameters that dynamic network elements such as switches
attempt to assure. In contrast, the testbeds in iCIRRUS are characterised by simple point-to-point
links.
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The simplest way to approximate impairments in fronthaul network impairment in a controlled
manner is to use an impairment generator that can impose variable latency, packet ordering, packet
loss and error injection.
Additionally, transport performance data from real networks can be analysed to approximate a
realistic fronthaul network context in which to operate the testbed. This would include, for example,
typical topologies, passive optical network (PON) split depth, and short and long term link
performance KPIs. Placing the measurements taken from the testbeds in a context representative of
a real-world fronthaul deployment allows the testbed results to be extrapolated to estimate real-
world performance.
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6 Conclusions An update of the iCIRRUS fronthaul architecture in terms of requirements, KPIs and building blocks
has been given in this deliverable, in order to meet the requirements of 5G (and beyond) mobile
networks in a cost effective and energy-efficient manner. The intelligent iCIRRUS fronthaul
architecture combines Ethernet as a transport protocol with the modification to the functional split
to reduce data rates in the fronthaul, while making statistical multiplexing gains possible, and thus
allows a more efficient use of the network resources. The intelligence in the proposed fronthaul will
assist in the provision of further services such as network-assisted D2D communications and mobile
cloud networking at the network edge. The various technical building blocks of the proposed
fronthaul solutions have been described and characterized in this deliverable. The investigations of
the time sensitive Ethernet technologies have demonstrated that the fronthaul latency requirements
can be met. Experimental evaluations of different modulation formats for data rates of 100G and
beyond have shown, that transmission lengths of 10 km can be achieved, which is within the
required range for a fronthaul application.
The architecture of the fronthaul network was reviewed in this deliverable. The work of WP5,
namely the different test scenarios, will also be based on the findings of WP3 and WP4 in the final
phase of iCIRRUS.
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References
[1] J-I Kani, J. Terada, K-I Suzuki, A. Otaka, “Solutions for future mobile fronthaul and access-network
convergence”, Journal of Lightwave Technology (2016) [early access article]
[2] P. Chanclou, L. Anet Neto, K. Grzybowski, Z. Tayq, F. Saliou, N. Genay, “Mobile Fronthaul Architecture
and Technologies: a RAN Equipment Assessment”, Th4B.1, OFC 2017, 19-23 March, 2017, USA
[3] Metro Ethernet Forum: MEF 22.1.1, Mobile Backhaul Phase 2, Amendment 1, July 2014
[4] A. Pizzinat, P. Chanclou, F. Saliou, T. Diallo, “Things you should know about fronthaul”, Journal of
Lightwave Technology, 1st March 2015, vol. 33, Issue: 5, 1077-1083 (2015).
[5] N. J. Gomes, P. Chanclou, P. Turnbull, A. Magee, V. Jungnickel “Fronthaul evolution: From CPRI to
Ethernet”, Optical Fibre Technology, Volume 26, Part A, December 2015, Pages 50–58
[6] M. Garyantes, “Virtual Radio Access Network opportunities and challenges”, 36th IEEE Sarnoff
Symposium, pp. 24-28 (2015)
[7] Z. Tayq, L. Anet Neto, B. Le Guyader, A. De Lannoy, M. Chouaref, C. Aupetit-Berthelemot, M.
Nelamangala Anjanappa, Si Nguyen, K. Chowdhury, P. Chanclou “Real Time Demonstration of the
Transport of Ethernet Fronthaul based on vRAN in Optical Access Networks”,Th3A.2, OFC 2017, 19-23
March, 2017, USA
[8] Common Public Radio Interface (CPRI): CPRI Specification V7.0 (2015-10-09),
- Open Base Station Architecture Initiative (OBSAI) : System specification v2.0
- Open Radio equipment Interface (ORI) : ETSI GS ORI 001 V4.1.1 (2014-10)
[9] Uwe Dötsch, Mark Doll, Hans-Peter Mayer, Frank Schaich, Jonathan Segel and Philippe Sehier,
“Quantitative Analysis of Split Base Station Processing and Determination of Advantageous Architectures
for LTE”, Bell Labs Technical Journal, Special Issue: General Papers, Volume 18, Issue 1, pages 105–128,
June 2013
[10] 3GPPP TR 38.801, Technical Specification Group Radio Access Network; Study on New Radio Access
Technology; Radio Access Architecture and Interfaces; (Release 14)
[11] Small Cell Forum, “Small Cell Virtualization Functional Splits and Use Cases,” January 2016
[12] Y.-S. Shiu, S. Y. Chang, H.-C. Wu, S. C.-H. Huang, and H.-H. Chen, “Physical layer security in wireless
networks: a tutorial," IEEE Wireless Communications, vol. 18, no. 2, pp. 6674, 2011.
[13] A. Biryukov, A. Shamir, and D. Wagner, “Real time cryptanalysis of a5/1 on a pc," in International
Workshop on Fast Software Encryption, pp. 1-18, Springer, 2000.
[14] D. Wagner, B. Schneier, and J. Kelsey, “Cryptanalysis of the cellular message encryption algorithm," in
Annual International Cryptology Conference, pp. 526-537, Springer, 1997.
[15] C. Adams and S. Lloyd, Understanding PKI: concepts, standards, and deployment considerations.
Addison-Wesley Professional, 2003.
[16] T. Austin, PKI: A Wiley Tech Brief. John Wiley & Sons, Inc., 2000.
[17] A. D. Wyner, “The wire-tap channel," The bell system technical journal, vol. 54, no. 8, pp. 1355-1387,
1975.
[18] T.-H. Chang, Y.-W. P. Hong, and C.-Y. Chi, “Training signal design for discriminatory channel
estimation," in Global Telecommunications Conference, 2009. GLOBECOM 2009. IEEE, pp. 1-6, IEEE,
2009.
[19] I. Csiszar and J. Korner, “Broadcast channels with confidential messages," IEEE transactions on
information theory, vol. 24, no. 3, pp. 339-348, 1978.
[20] “Time-Sensitive Networking for Fronthaul,” IEEE Standard P802.1CM [Online]. Available:
http://www.ieee802.org/1/pages/802.1cm.html
[21] M. K. Al-Hares, P. Assimakopoulos, S. Hill, and N. J. Gomes “The Effect of Different Queuing Regime
On a Switched Ethernet Fronthaul,” in IEEE Int. Conf. on Transp .Optic.Net. (ICTON), Trento, Italy,
2016, pp. 1-4
[22] “Frame Preemption," IEEE standard 802.1Qbu [Online]. Available:
http://www.ieee802.org/1/pages/802.1bu.html.
[23] "Enhancements for Scheduled Traffic," IEEE standard 802.1Qbv [Online]. Available:
http://www.ieee802.org/1/pages/802.1bv.html
[24] T. Wan and P. Ashwood-Smith, “A Performance Study of CPRI over Ethernet with IEEE 802.1Qbu and
802.1Qbv Enhancements,” in Global Commun. Conf. (GLOBECOM), San Diego, CA, 2015, pp. 1-6
Contract No: 644526 iCIRRUS 1 Jan 2015 – 31 Dec 2017
Page 74 of 78
This project has received funding from the European
Union’s Horizon 2020 research and innovation
programme under grant agreement No 644526
[25] M. K. Al-Hares, P. Assimakopoulos, D. Muench and N. J. Gomes, “Scheduling in an Ethernet Fronthaul
Network,” in European Conf. on Networks and Commun. (EUCNC), Oulu, Finland, 2017. Accepted for
publication
[26] L. Fernandez del Rosal, V. Jungnickel, D. Muench, H. Griesser, P. Assimakopoulos, N. Gomes, Y. Kai, H.
Thomas, M. Parker, C. Magurawalage, K. Wang, P. Chanclou and V. D, “D3.2 iCirrus - Preliminary
Fronthaul Architecture Proposal,” 2016.
[27] R. Veisllari, S. Bjornstad, J. P. Braute, K. Bozorgebrahimi and C. Raffaelli, "Field-Trial Demonstration of
Cost Efficient Sub-wavelength Service Through Integrated Packet Circuit Hybrid Network," Journal of
Optical Communications and Networking, 2015.
[28] “IEEE P802.3bs/D1.2 Draft Standard for Ethernet Amendment: Media Access Control Parameters,
Physical Layers and Management Parameters for 400 Gb/s Operation,” pp. 1–269, 2016. [Online].
Available: http://www.ieee802.org/3/bs/
[29] M. Scholten, T. Coe, and J. Dillard, “Continuously-interleaved BCH (CI-BCH) FEC delivers best in class
NECG for 40G and 100G metro applications,” in 2010 Conference on Optical Fiber Communication
(OFC/NFOEC), collocated National Fiber Optic Engineers Conference, 2010, paper NTuB3.
[30] J. Wei et al., “400 Gigabit Ethernet Using Advanced Modulation formats: Performance, Complexity, and
Power Dissipation, “Commun. Mag. 53(2), 182 (2015)
[31] W. Kobayashi et al., “25 Gbaud/s 4-PAM (50 Gbit/s) modulation and 10 km SMF transmission with 1.3
µm InGaAlAs-based DML,” Electron. Lett., 2014, 50, (4), pp. 299-300
[32] H. Huang et al., “An 8-bit 100 GS/s Distributed DAC in 28 nm CMOS for Optical Communications,”
Trans. Microw. Theory. 63(4), 1211 (2015)
[33] X. Chen et al., “All-electronic 100-GHz Bandwidth Digital-to-Analog Converter Generating PAM Signals
up to 190-GBaud,’ Proc. OFC, Th5C.5 (2016)
[34] C. Kottke et al., “178 Gbit/s Short-Range Optical Transmission Based on OFDM, Electrical Up-
Conversion and Signal Combining,” Proc. ECOC, W.4.P1.SC5 (2016)
[35] S. Kanazawa et al., “Transmission of 214-Gbit/s 4-PAM signal using an ultrabroadband lumped-electrode
EADFB laser module,” Proc. OFC, Th5B.3 (2016)
[36] H. Yamazaki et al., “300-Gbps Discrete Multi-tone Transmission Using Digital-Preprocessed Analog-
Multiplexed DAC with Halved Clock Frequency and Suppressed Image,” Th3B.4, ECOC 2016
[37] D. Chang et al., “LDPC convolutional codes using layered decoding algorithm for high speed coherent
optical transmission,” OW1H.4, OFC, 2016
[38] M. Sauer, A. Kobyakov, and J. George, "Radio over fibre for picocellular network architectures," IEEE
Journal on Lightwave Technology, vol. 25, no. 11, 2007.
[39] "5G: Personal Mobile Internet beyond What Cellular Did to Telephony", Gerhard Fettweis, Siavash
Alamouti, IEEE Communication Magazine, February 2014.
[40] T. Marzetta, "Noncooperative cellular wireless with unlimited numbers of base station antennas," IEEE
Transactions on Wireless Communications, vol. 9, no. 1, pp 3590-3600, Nov 2010.
[41] M.C. Parker et al. CHARISMA: "Converged Heterogeneous Advanced 5G Cloud-RAN Architecture for
Intelligent and Secure Media Access," EuCNC2016.
[42] Upamanyu Madhow. "Broadband Millimeter Wave Networks: Architectures and Applications." 2008 2nd
International Symposium on Advanced Networks and Telecommunication Systems. Pages: 1 - 3
[43] Terence Quinlan and Stuart Walker. "A Monopole Fed Omnidirectional 13dBi Gain Bi-Conical Horn
Antenna for IEEE802.11ad Applications," Loughborough Antennas and Propagation Conference, Nov
2016.
[44] Constantine Balanis. Antenna Theory, Third Edition. Wiley. Pages 498 - 501.
[45] O.B. Jacobs, J.W. Odendaal and J. Joubert. "Analysis and Design of a wideband omnidirectioal Antenna."
Microwave and Optical Technology Letters, June 2011. wileyonlinelibrary.com. DOI 10.1002/mop.25990
[46] Sheldon S. Sandler and Ronold W. P. King. "Compact Conical Antennas for Wide-Band Coverage." IEEE
Transactions on Antennas and Propagation, Vol. 42, No. 3, March 1994. Pages 436 - 439.
[47] W. D. Burnside and C. W. Chuang, "An aperture-matched horn design," IEEE Trans. Antennas Propag.,
Vol. AP-30, No. 4, Jul. 1982 Pages. 790-796.
[48] Constantine Balanis. Antenna Theory, Third Edition. Wiley. Pages 792 -794.
[49] Matthew A. Morgan and Tod A. Boyd. "A 10-100-GHz Double-Ridged Horn Antenna and Coax
Launcher," IEEE Transactions on Antennas and Propagation, Vol. 63, No. 8, Aug. 2015, Pages 3317 - 34.
[50] P. Turnbull, H. Thomas, D. Venmani, P. Chanclou, V. Jungnickel, L. Fernandez del Rosal, M. Parker, P.
Assimakopouluos, K. Kenan Al-Hares and N. Gomes, “D3.1 iCIRRUS - Verification of Ethernet as
transport protocol for fronthaul / midhaul,” 2015.
Contract No: 644526 iCIRRUS 1 Jan 2015 – 31 Dec 2017
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This project has received funding from the European
Union’s Horizon 2020 research and innovation
programme under grant agreement No 644526
[51] L. Fernandez del Rosal, V. Jungnickel, D. Muench, H. Griesser, P. Assimakopoulos, N. Gomes, Y. Kai, H.
Thomas, M. Parker, C. Magurawalage, K. Wang, P. Chanclou and V. D, “D3.2 iCirrus - Preliminary
Fronthaul Architecture Proposal,” 2016.
[52] M. Hinrichs, L. Fernandez del Rosal, C. Kottke, and V. Jungnickel, “Analog vs. Next-Generation Digital
Fronthaul: How to Minimize Optical Bandwidth Utilization,” 21st International Conference on Optical
Network Design and Modeling, 2017.
[53] M. Hinrichs, L. Fernandez del Rosal, C. Kottke, V. Jungnickel, and R. Freund, “Experimental
Investigation of New Fronthaul Concepts for 5G,” 11. ITG-Fachkonferenz Breitbandversorgung in
Deutschland, 2017.
[54] Fernández del Rosal, L., Habel, K., Weide, S., Wilke Berenguer, P., Jungnickel, V., Farkas, P., Freund, R.:
Multi-Gigabit Real-Time Signal Processing For Future Converged Networks. ITG-Fachkonferenz Breit-
bandversorgung in Deutschland 2016
[55] ETSI Technical Specification 136 104 v12.6.0 (2015-05)
[56] Gliese, U., Nørskov, S. and Nielsen, T.N.: Chromatic Dispersion in Fiber-Optic Microwave and
Millime¬ter-Wave Links. IEEE Transactions on Microwave Theory and Techniques 44.10 (1996), pp.
1716–1724
[57] Howard Thomas, et al. “D3.3 SLA and SON Concept for iCIRRUS”, 2016
Contract No: 644526 iCIRRUS 1 Jan 2015 – 31 Dec 2017
Page 76 of 78
This project has received funding from the European
Union’s Horizon 2020 research and innovation
programme under grant agreement No 644526
List of figures Figure 1: Optical access solutions for backhaul based on 1) PtP, 2) T(W)DM PON, 3) PtP WDM PON ................ 13
Figure 2: Mobile Backhaul, Midhaul and Fronthaul from MEF [3]. ...................................................................... 15
Figure 3: First three steps of mobile equipment evolution. ................................................................................. 15
Figure 4 Optical access solutions for low layer fronthaul based on a) PtP fibre, b) PtP WDM PON..................... 17
Figure 5: Optical access solutions based for high layer fronthaul based on: 1) PtP, b) T(W)DM-PON, 3) PtP
WDM-PON. .................................................................................................................................................. 18
Figure 6: Several high layer split fronthaul interfaces for the downlink and uplink. ............................................ 21
Figure 7: Comparative architecture views of 4G (centralised) and 5G (decentralised) networks. ....................... 24
Figure 8 (a) Different LTE functional subdivisions (function splits) options, (b) The implemented split processing
module subdivision and (c) the implemented split networking entity subdivision. .................................... 28
Figure 9 (a) The evolved fronthaul and (b) high-level view of the buffering stages and measurement interface
points ........................................................................................................................................................... 29
Figure 10 Fronthaul and application (OTG traffic generator) data rate measurement results for different
numbers of UEs. The traffic generator is producing traffic only for the downlink direction. ...................... 32
Figure 11 Data rates and percentage increases at different points in the processing chain, for three different
tests of ascending application layer data rates. .......................................................................................... 32
Figure 12 Fronthaul processing latency per LTE subframe for different numbers of UEs .................................... 33
Figure 13 The fronthaul intelligent processing unit (IPU) and the different feeds from the fronthaul and OTA
sections ........................................................................................................................................................ 34
Figure 14 The result of SDN-enabled traffic steering of the background traffic in an evolved fronthaul on the
latency and latency variation of the split traffic. Three packet type traces are shown, pkt_DCI (downlink
control information), pkt_DLSCH (downlink-shared channel) and pkt_SI (system information). ............... 35
Figure 15 Example of live KPI performance monitoring. ...................................................................................... 36
Figure 16: FUSION: Exploiting the inter-packet gaps between HP frames to transmit LP frames ....................... 37
Figure 17: FUSION: Test setup investigating latency and latency variation ......................................................... 37
Figure 18: FUSION: Theoretical estimation of the latency and latency variation of the FUSION IP ..................... 38
Figure 19: FUSION: Theoretical latency and latency variation for aggregation, neglecting PHY and MAC delays
and cable delays ........................................................................................................................................... 39
Figure 20: FUSION: Theoretical latency and latency variation for aggregation and deaggregation neglecting PHY
and MAC delays and cable delays ................................................................................................................ 40
Figure 21: FUSION: Theoretical end-to-end latency and latency variation including PHY/MAC delays and
neglecting cable delays for GST links ........................................................................................................... 41
Figure 22: FUSION: Theoretical end-to-end latency and latency variation including PHY/MAC delays and
neglecting cable delays for SM links ............................................................................................................ 41
Figure 23: FUSION: Measured results for latency and latency variation using an MTU of 16000 Byte for GST and
a packet size of 9622 Byte for SM ................................................................................................................ 42
Figure 24 (a) Reference architecture for the time-aware shaper use-cases presented in this work and (b)
Scheduling design concept........................................................................................................................... 43
Figure 25 Generic time window, window section and subsection plan based on IEEE 802.1Qbv ....................... 44
Figure 26 Network Scenario implemented in Opnet. All network interfaces are 1 GbE ..................................... 45
Figure 27 Average and peak FDV for the PTP traffic with SP and TAS with different GPs. The background traffic
source is constant frame-rate and constant frame size .............................................................................. 46
Figure 28 Average and peak FDV for the PTP traffic with SP and TAS with different GPs. The background traffic
source is constant frame-rate with a varying frame size following a normal distribution with mean of 1000
octets and variance of 200 octets ................................................................................................................ 47
Contract No: 644526 iCIRRUS 1 Jan 2015 – 31 Dec 2017
Page 77 of 78
This project has received funding from the European
Union’s Horizon 2020 research and innovation
programme under grant agreement No 644526
Figure 29 Average and peak FDV for the PTP traffic with SP and TAS with different GPs. The background traffic
source is constant frame-rate with a varying frame size following a normal distribution with mean of 1000
octets and variance of 500 octets ................................................................................................................ 47
Figure 30 Zoom-in in the region of GPs from 0 to 1 μs for the results of Figure 29. The inset shows the worst-
case PTP time-stamping error that would result from the peak FDV values ............................................... 48
Figure 31: Experimental transmission setup for 100G. ........................................................................................ 49
Figure 32: a) DSP blocks of the implemented DMT system and b) i) estimated SNR per subcarrier at the receiver
optical back-to-back and the applied ii) bit loading and iii) power loading ................................................. 50
Figure 33: Transmission results of DMT at different data rates and for different transmission distances. ......... 50
Figure 34: a) DSP blocks of the PAM-4 system, b) digital PSD of transmit signal and c) eye diagram after the
EML. ............................................................................................................................................................. 51
Figure 35: Optical back-to-back transmission results of 112Gb/s PAM-4 employing different numbers of pre-
and post-FFE coefficients: a) shows the optical eye diagrams obtained directly after the EML using a pre-
equalizer of 5 coefficients and 61 coefficients, b) and c) illustrate the BER vs. ROP results using different
numbers of post-FFE coefficients and d) depicts the BER performance for different Tx-FFE/Rx-FFE
combinations at an input power of 0dBm. .................................................................................................. 52
Figure 36: Transmission results of 112 Gb/s PR PAM-4: a) using different MLSE memory length after the FFE in
case of optical back-to-back transmission ................................................................................................... 52
Figure 37: System concept for spectral up-conversion utilizing IQ mixers and several independent sub-bands. a)
Electrical spectrum after DAC, b) after IQ-mixing, c) after signal combining, d) after optical modulation. 53
Figure 38: Experimental setup for spectral up-conversion based IM/DD transmission links. .............................. 54
Figure 39 (a+b) Measured BERs for PAM/QAM modulation at different fibre lengths (scenario #1). (c+d)
Measured data rates for different target BERs using DMT/OFDM (scenario #2). ....................................... 55
Figure 40: (a+b). Measured BERs for PAM and DMT at 80 and 120 Gbit/s for the lower band at different fibre
lengths. (c+d) Measured BERs for QAM and OFDM at 64 and 80 Gbit/s for the upper band at different
fibre lengths. ................................................................................................................................................ 56
Figure 41: Antenna model graphic showing conical top and bottom sections with integral matching rings,
antenna feeding arrangements and modified support pillars, with surface current plot showing
dissipation effect of the matching rings. ..................................................................................................... 57
Figure 42: Antenna assembly, showing all component parts and the support pillars and probe clamping
arrangement. ............................................................................................................................................... 58
Figure 43: Measured S11 return loss over the IEEE 802.11ad frequency range with markers at four spot
frequencies. ................................................................................................................................................. 59
Figure 44: Corresponding Smith chart showing measured antenna feed impedance values at four spot
frequencies .................................................................................................................................................. 59
Figure 45: Comparison of co-polar and cross-polar transmission characteristics between 30 GHz and 65 GHz,
also illustrating the wide bandwidth capability of the omnidirectional antenna. ....................................... 61
Figure 46: Showing four channel capability over an operating angle of 360 degrees, demonstrating backhaul,
mobile, and mesh deployment usages. ....................................................................................................... 61
Figure 47: Signal processing for analogue fronthaul ............................................................................................ 65
Figure 48: Experimental setup (optical link) ......................................................................................................... 66
Figure 49: Signal processing for evolved digital fronthaul.................................................................................... 66
Figure 50: Estimated EVM for analogue fronthaul over Rx power and for different fibre lengths ...................... 68
Figure 51: Estimated BER for digital fronthaul over Rx power and for different fibre lengths ............................ 68
Figure 52: Estimated BER for analogue fronthaul over Rx power and for different fibre lengths ....................... 68
Contract No: 644526 iCIRRUS 1 Jan 2015 – 31 Dec 2017
Page 78 of 78
This project has received funding from the European
Union’s Horizon 2020 research and innovation
programme under grant agreement No 644526
List of tables Table 1: Compilation of Fronthaul KPIs ................................................................................................................ 26
Table 2: The 28 (32)-Octet common packet header for all packets sent/received through the fronthaul
interface. ...................................................................................................................................................... 30
Table 3: Ethernet frame payload fields for PKT_DLSCH ....................................................................................... 30
Table 4: DMT System Parameters. ....................................................................................................................... 49
Table 5: Comparison of Peripheral Measured Antenna Gain ............................................................................... 60
Table 6: Characteristics of measurements in fronthaul domain .......................................................................... 63
Table 7: Characteristics of measurements in RAN domain .................................................................................. 64