Cellular Back Hauling Optimization
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Transcript of Cellular Back Hauling Optimization
8/4/2019 Cellular Back Hauling Optimization
http://slidepdf.com/reader/full/cellular-back-hauling-optimization 1/31
Cellular Backhauling
Optimization
Yaakov (J) SteinChief Scientist
RAD Data Communications, Ltd.
8/4/2019 Cellular Back Hauling Optimization
http://slidepdf.com/reader/full/cellular-back-hauling-optimization 2/31
Cellular communications
To most people cellular communications means only the air interface
This is the Radio Frequency link between MS and BTS
Mobile Station
Base Transmitter Station
air interface
cell site
8/4/2019 Cellular Back Hauling Optimization
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Cellular networks
But there is a lot more to the cellular network than that !
Using GSM (2G) terminology :
All the Base Transmitter Stations are to Base Station Controllers
The BSCs are connected to Mobile Switching Centers
MSCs are interconnected,
and also connected to the Public Switched Telephony Network
PSTN
MSC
BTS
BTS
BTS
BTS
BTS
BTS
BTSBTS
BTS
BSC
BSC
BSC
MSC
HLR VLR
8/4/2019 Cellular Back Hauling Optimization
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Cellular backhauling
We (informally) call all of the network except the air interfacethe cellular backhaul network
Backhauling of 2G cellular traffic uses TDM (E1/T1) links over :• Copper• Fiber• Microwave
Due to rapid worldwide increase in cellular penetration
backhauling is one of the hottest topics in the telecommunications industry
To reduce operational expenses, cellular operators want to :
• reduce bandwidth consumption
• migrate to (less expensive) Packet Switched Networks (IP/MPLS/Ethernet)• employ less expensive transport types, for example
– Metro Ethernet Networks – DSL links
Reduction of bandwidth (optimization ) for 2G GSM is the main topic of this talk
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Cellular backhaul optimization
Voice traffic is already compressed by the mobile station
So why can cellular traffic be optimized at all ?
• TDM transport mechanisms can not reduce bandwidth
• standard user traffic (TRAU) formats are extremely inefficient
• nonactive user channels are sent
• silence/idle frames in active channels are sent
• signaling channels (HDLC-based) are inefficient
• data can be compressed by lossless data compression
• additional mechanisms (e.g. stronger compression) may sometimes be used
ACE-3xxx
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Cellular backhaul transport
When TDM transport (e.g. E1 links) is used – optimization enables use of fewer E1s
to carry the same amount of user traffic
– reduced operational expense at dense portions of network
– however, compressed traffic formats are not standardized
When TDM transport is replaced with Packet Switched Networks
– service less expensive to begin with
– service often charged by bandwidth used
–
optimization enables using only the minimum BW needed – operational expense reduced
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GSM 2G architecture
GSM formally separates the Public Land Mobile Network into subsystems
and defines the interfaces / protocols between each two pieces of equipment
A-type interfaces carry the voice traffic in the backhaul portion of the network
• A interface is a standard TDM link divided into 64 kbps timeslots• Abis interface connects the BTS to the BSC and carries FR or HR channels• Ater interface connects the BSC to the MSC and carries FR or HR channels
A-type interfaces also carry control information
BTS BSC MSCBase Station
Subsystem
Network and
Switching Subsystem
Um
interface
Abis
interface
A / Ater
interface
B … F interfaces
RF TDM TDM
serversand
other networks
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Carrying A-type interfaces over PSN
Cellular operators can transport Abis/Ater over PSNs instead of TDM
To do this without forklift upgrade of their equipment to 3G
they can use pseudowire (PW) technology
A PW emulates a native service by building a tunnel through the PSN
Bandwidth reduced as compared to TDM
with optimization, bandwidth can be further reduced
BTS BSC
Abis
interfaceTDM
Abis
interfaceTDM
PSN
pseudowire (PW)
cellopt GW cellopt GW
8/4/2019 Cellular Back Hauling Optimization
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Voice channelsAlthough over time new services were added•
Fax• Short Message Service• Multimedia Message Service• Wireless Application Protocol• Internet and WWW access• Video streaming
the cellular network was originally designed for voice trafficA GSM transmitter segments voice into 20 millisecond frames
And applies compression to place voice traffic into one of two channel types• Full Rate channel - 16 kbps = 2 bits every 1/8000 sec. = 320 bits per 20 ms.• Half Rate channel - 8 kbps = 1 bit every 1/8000 sec. = 160 bits per 20 ms.
There are various compression algorithms
• Full Rate codec - 13 kbps (FR channel)• Enhanced Full Rate codec - 12.2 kbps (FR channel)
• Half Rate codec - 5.6 kbps (HR channel)• Adaptive MultiRate - 4.75, 5.15, 5.9, 6.7, 7.4, 7.95 (HR or FR channels)
- 10.2, 12.2 kbps (FR channel)
8/4/2019 Cellular Back Hauling Optimization
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TRAU framesThe compressions and format conversions in the network are performed by the
Transcoder and Rate Adaptation UnitInformation on the A bis and A ter interfaces is encoded in TRAU frames
TRAU voice frames represent 20 ms. of audio
• FR channels - TRAU frames are 320 bits = 40 bytes
• HR channels - TRAU frames are 160 bits = 20 bytes
The TRAU frames are transported over FR and HR channels
…
…
…
...
TDM (E1) frame (256 bits)
t8 bit TDM timeslots1 bit HR timeslots2 bit FR timeslots
…
Note that a full E1 (2 Mbps)must be used even when thereare very few channels
idle = 01
alarm = 00
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TRAU framing
The TRAU frames have a specific frame structure that must be detected
For example, this is the framing of the generic FR (40 byte) TRAU frame :
And this is the generic HR (20 byte) TRAU frame :
Note: there are other frame formats as well
00000000 00000000 1xxxxxxx xxxxxxxx 1xxxxxxx xxxxxxxx 1xxxxxxx xxxxxxxx
1xxxxxxx xxxxxxxx 1xxxxxxx xxxxxxxx 1xxxxxxx xxxxxxxx 1xxxxxxx xxxxxxxx
1xxxxxxx xxxxxxxx 1xxxxxxx xxxxxxxx 1xxxxxxx xxxxxxxx 1xxxxxxx xxxxxxxx
1xxxxxxx xxxxxxxx 1xxxxxxx xxxxxxxx 1xxxxxxx xxxxxxxx 1xxxxxxx xxxxxxxx
1xxxxxxx xxxxxxxx 1xxxxxxx xxxxxxxx 1xxxxxxx xxxxxxxx 1xxxxxxx xxxxTTTT
00000000 1xxxxxxx 01xxxxxx 1xxxxxxx
1xxxxxxx 1xxxxxxx 1xxxxxxx 1xxxxxxx
1xxxxxxx 1xxxxxxx 1xxxxxxx 1xxxxxxx
1xxxxxxx 1xxxxxxx 1xxxxxxx 1xxxxxxx
1xxxxxxx 1xxxxxxx 1xxxxxxx 1xxxxxTT
x bits are data / control
and are not part of the framing
T bits are for time alignment(justification)
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Abis signaling channels
It is very important not to delay or corrupt special signaling channelsAter signaling channels are based on SS7
Abis signaling channels are not completely standardized
each equipment vendor has its own signaling format
Abis Signaling channels can be
• 16 kbps (2 bits per TDM frame)• 32 kbps (4 bits per TDM frame)
• 64 kbps (a full 8 bit TDM timeslot)
Signaling is usually HDLC based, with a frame format :
The frame between flags (7E hex) is bit-stuffed
Between frames there may be flags or other filling
flag address ctrl DATA CRC flag
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Backhauling data
User data can be transported over the Abis interface in various ways
Low rate data (up to 9.6 or 14.4 kbps) is transported in TRAU frames
Intermediate rates (up to 114 kbps) are available via GPRS (2.5G)
Higher rates (theoretically up to 384 kbps) via EDGE (2.75G)
GPRS / EDGE are carried over G-type interfaceswhich may share the same TDM link as A-type interfaces
GPRS/EDGE bandwidth allocation may be dynamic
it takes over bits not used by the A-type interfaces
In 3G networks data can be much higher rate (over 2 Mbps, e.g. 10 Mbps)
carried over I-type interfacesthat use separate transport media
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GSM 2.5G architecture (GPRS/EDGE)
The first high-speed GSM data (WAP, PTT, MMS, WWW) service was
the Generalized Packet Radio Service
It provides 56 kbps - 114 kbps packet data for IP communications
The air interface is enhanced, but won't be discussed here
To the GSM backhaul architecture is adds• Serving GPRS Support Node• Gateway GPRS Support Node• G interfaces
The next stage is Enhanced Data for Global Evolution (AKA Enhanced GPRS)
BTS
MSCUm
A bis A / A ter
BSC
Gb
BSS
SGSN
Gn
NSS-CS
GGSNNSS-PS
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3+G architectures
In initial 3G releases Iu interfaces are based on ATM (Iu-CS:AAL2, Iu-PS:AAL5)
In the final phases, the network becomes IPand the protocols become VoIP
At that point the window of opportunity for optimization closes
Node B RNC
Uu Iub
Iu - CS
RF
User Equipment
3G-MSC
3G-SGSN
Iu - PSPDN
PSTN
UTRAN Core
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1st challenge - channel detectionVoice/data/signaling information appears at various places in the frame
Were we to understand the proprietary signaling – we would know where to look for the various channels – but this signaling is vendor-dependent – and the formats are not always known
So we need to employ an intelligent detector/classifier/deframer
– detect channel framing and return field positions – classify channel as voice/data/signaling/idle/unknown – maintain relative synchronization
Matching framer at egress needs to recreate the original frames
signalingHR voiceFR voice64K dataTDM sync FR voice 32K data
…
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Channel detector/classifierThis detector/classifier needs to continually scan all
• 1-bit positions for HR TRAU frames• even aligned 2-bit fields for FR TRAU frames• even aligned 2-bit fields for HDLC• nibble-aligned nibbles for HDLC• byte-aligned octets for HDLC•
fields of idle bits• anything else
and then return the identifications and positions found
Unidentified non-idle information must be reliably transported
The processing involves
• searching for specific bit combinations• performing bit correlations
and is extremely computationally intensive
Can be performed by a DSP with good bit-oriented operations
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2nd challenge - optimization
Once the various components have been foundthe information needs to be reduced in size and reliably transported
• Idle fields need not be sent, often accounting for a large BW reduction
• TRAU framing overhead may be removed
• Voice frames marked as silent (DTX) may be suppressed• Voice Activity Detection may be employed to suppress silence
• HDLC flags are removed and the contents destuffed
• Data may be compressed
We will deal with each of these in turn
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3rd challenge - data compression
Data is typically transported over cellular networks in uncompressed form
Lossless data compression algorithms, e.g.
• Ziv-Lempel variants
• Huffman codes / arithmetic codes
• Shannon-Fano coding• Burrows-Wheeler Transform
• Prediction by Partial Match
can be an effective optimization method
when there is a significant amount of data traffic
Text data, such as HTML or WML, can be significantly compressed
Compressed video, binary files, encrypted data, etc.
can not be compressed
8/4/2019 Cellular Back Hauling Optimization
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Using data compression
Many algorithms perform well when there is a lot of data
The problem is that the impact of packet loss must be taken into account
If we compress each packet separately
• there is not enough data for efficient compression
If we keep history from previous packets
• we need to separate flows
• we need to store state
• loss of single compressed packet causes multiple packets to be discarded
DSPs can be exploited to handle data compression
main limitation - large amount of memory needed
Need a DSP with efficient bit/byte-oriented operations
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4th challenge - trans-rating
Audio / video streams are already compressedFurther compression may not be possible
However, sometimes there are hard bandwidth limits (caps )
and we must be able to survive short bandwidth peaks
In certain instances trans-rating may be useful
• at the expense of reduced perceived quality
• especially when exceeding the cap is expected to be extremely rare
For example
• change compression rate for AMR family on a frame-by-frame basis• transcode EFR codec down to a lower AMR rates
and transcode back up at network egress
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Smart trans-rating
The simplest (but most computationally intensive) way to trans-rateis to cascade a decoder and an encoder
For a particular pair of codecs there may be better ways, with
• lower computational complexity
• lower delay• less perceptual degradation
For AMR, there are commonalities that may be exploited
However, reserving DSP computational resources
is usually not economically justifiable
for a process that will only be used for short bandwidth peaks
Other mechanisms may be more affordable, such as smart frame drop
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5th challenge - smart frame drop
Sometimes transport traffic bandwidth has a hard capIf this cap is exceeded, voice frames will be discarded
The TRAU will employ Packet Loss Concealment techniques
– that cover up much of the effect
– generally there will still be noticeable impact on the user experience
A smarter technique is smart selective frame drop (extended VAD)
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Smart frame drop
Instead of dropping randomly chosen voice frames … we can carefully select the frames to dropusing a criterion of least perceptual quality degradation
The selection can be based on voice parameters in the TRAU frame
without full decoding of the voice coding
The resulting DSP code
• is codec-dependent• requires saving of state information per channel• but does not require large amounts of memory
The smart frame drop mechanismshould be tightly coupled controlled to the main control functionso that only the minimal percentage of frames are dropped
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6th challenge - timing recovery
TDM's physical layer transfers accurate frequency (sync) informationGSM BTSs use the accurate frequency recovered from the TDM link to
• generate accurate radio frequencies
• generate symbol timing
• send time offset information to mobile stations
• ensure short handover when moving from cell to cellCDMA and 3G cellular systems also need accurate Time Of Day
Requirements are stringent :
• absolute frequency accuracy must be better than 50 ppb
• jitter and wander need to conform to ITU TDM standards
• 3G stations need time accuracy of better than 3 s• 3G TDD mode requires time accuracy of better than 1.25 s from UTC
When replacing TDM links with PWs over PSNs we lose timing information
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Frequency measuresFrequency needs to be stable and accurate
and there may be both frequency jitter and wander
jitter is easy to filter out - the real problem is wander
f f
time
f
time
f
timetime
stablenot accurate
not stablenot accurate accurate
but not stablestable andaccurate
Jitter = short term timing variation
(i.e. fast jumps - frequency > 10 Hz)
Wander = long term timing variation
(i.e. slow moving - frequency < 10 Hz)
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PSN - Delay and PDV
TDM frequency distribution is based on constant bit rate
Packets in PSNs may be sent at a constant rate
but PSNs introduce Packet Delay Variation
PDV makes frequency recovery difficult
PSN
but arrival timesare not uniform transmission timesmay be uniform
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Jitter Buffer
Data from arriving packets are written into a jitter buffer
Once buffer is 1/2 filled, we read from buffer and output to Abis interface
Data is read from jitter buffer at a constant rate - so no jitterBut how do we know the correct rate ?
How do we guard against buffer overflow/underflow ?
We need a frequency recovery algorithm
Jitter Buffer
PSN
but arrival timesare not uniform transmission timesmay be uniform
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Frequency recovery
Packets are injected into network ingress at times Tn
The source packet rate R is constant
Tn = n / R
The PSN delay D n can be considered to be the sum oftypical delay d and random delay variation V n
The packets are received at network egress at times tntn = Tn + D n = Tn + d + V n
By proper averaging/filtering<tn > = Tn + d = n / R + d
and the original packet rate R has been recovered
Unfortunately, simple averaging would be much too slow
By the time the accuracy would be sufficient, the rate would have wanderedIn such cases control loops (PLL, FLL) are commonly used
but the noise is much higher here than in usual cases where PLLs are used
and changing frequency to compensate for inaccuracy causes wander
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Frequency recovery algorithms
Early solutions relied on : – linear regression – augmented PLLs – FLL - PLL hybrids
More sophisticated implementations exploit :
– parameter estimation and tracking – oscillator modeling – network modeling – system separation
Although the algorithms may be complex
– they run at a relatively low rate (tens of times per second)
– and can thus be run on a DSP
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SummaryCellular backhaul optimization enables
– more efficient use of overloaded transport infrastructures – lowering of OPEX
Cellular optimization is applicable to 2G and 2.5G networks
There are many challenges to building an operational system
– channel detection, classification, and deframing – packet-loss-tolerant data compression
– smart trans-rating
– smart selective frame drop
–timing recovery
DSPs provide a good platform for meeting these challenges
For more information, visit www.RAD.com