◆ Self-Optimization of LTE Networks UtilizingCelnet XplorerArumugam Buvaneswari, Lawrence Drabeck, Nachi Nithi, Mark Haner, Paul Polakos, and Chitra Sawkar
In order to meet demanding performance objectives in Long Term Evolution(LTE) networks, it is mandatory to implement highly efficient, autonomicself-optimization and configuration processes. Self-optimization processeshave already been studied in second generation (2G) and third generation(3G) networks, typically with the objective of improving radio coverage andchannel capacity. The 3rd Generation Partnership Project (3GPP) standard forLTE self-organization of networks (SON) provides guidelines on self-configuration of physical cell ID and neighbor relation function and self-optimization for mobility robustness, load balancing, and inter-cellinterference reduction. While these are very important from an optimizationperspective of local phenomenon (i.e., the eNodeB’s interaction with itsneighbors), it is also essential to architect control algorithms to optimize thenetwork as a whole. In this paper, we propose a Celnet Xplorer-based SONarchitecture that allows detailed analysis of network performance combinedwith a SON control engine to optimize the LTE network. The networkperformance data is obtained in two stages. In the first stage, data isacquired through intelligent non-intrusive monitoring of the standardinterfaces of the Evolved UMTS Terrestrial Radio Access Network (E-UTRAN)and Evolved Packet Core (EPC), coupled with reports from a software clientrunning in the eNodeBs. In the second stage, powerful data analysis isperformed on this data, which is then utilized as input for the SON engine.Use cases involving tracking area optimization, dynamic bearer profilereconfiguration, and tuning of network-wide coverage and capacityparameters are presented. © 2010 Alcatel-Lucent.
Long Term Evolution standard. Through its E-UTRAN,
LTE is expected to substantially improve end-user
throughputs and sector capacity and reduce
user plane latency, bringing significantly improved user
experience with full mobility.
IntroductionThe recent increase of mobile data usage and
emergence of new applications such as multimedia
online gaming (MMOG), mobile television (TV), Web
2.0, and content streaming have motivated the 3rd
Generation Partnership Project (3GPP) to enhance the
Bell Labs Technical Journal 15(3), 99–118 (2010) © 2010 Alcatel-Lucent. Published by Wiley Periodicals, Inc. Published online in Wiley Online Library (wileyonlinelibrary.com) • DOI: 10.1002/bltj.20459
100 Bell Labs Technical Journal DOI: 10.1002/bltj
With the emergence of Internet Protocol (IP) as
the protocol of choice for carrying all types of traffic,
LTE is scheduled to provide support for IP-based traf-
fic with end-to-end quality of service (QoS). Voice
traffic will be supported mainly as Voice over IP
(VoIP), enabling better integration with other multi-
media services. Evolved UMTS terrestrial radio access
(E-UTRA) is expected to support different types of
Panel 1. Abbreviations, Acronyms, and Terms
2G—Second generation3G—Third generation3GPP—3rd Generation Partnership Project4G—Fourth generationAPN—Access point nameARP—Allocation and retention priorityBE—Best effortBLER—Block error rateCAN—Connectivity access networkCDMA—Code Division Multiple AccessCN—Core networkCQI—Channel quality indicatorCX—Celnet XplorerDC—Data collectorsDL—DownlinkDPI—Deep packet inspectioneAN—Evolved access networkeHRPD—Evolved HRPDEMM—Evolved mobility managementeNB—Enhanced node BEPC—Evolved Packet CoreEPS—Evolved Packet SystemE-RAB—E-UTRAN radio access bearereRNC—Enhanced radio network controllerESM—Evolved Session ManagementE-UTRA—Evolved UMTS Terrestrial Radio
AccessE-UTRAN—Evolved UMTS Terrestrial Radio
Access NetworkEV-DO—Evolution Data OptimizedFTP—File Transfer ProtocolGBR—Guaranteed bit rateGUTI—Globally unique temporary identityGW—GatewayHRPD—High rate packet dataHSGW—HRPD serving gatewayHSS—Home subscriber serverIMSI—International mobile subscriber identityIP—Internet ProtocolKPI—Key performance indicatorLTE—Long Term EvolutionMAC—Medium access controlMBR—Maximum bit rateMCS—Modulation and coding scheme
MIMO—Multiple input multiple outputMME—Mobility management entityMMOG—Multimedia online gamingNAS—Non-access stratumOAM—Operations, administration, and
maintenanceOPEX—Operational expendituresPC—Personal computerPCF—Packet control functionPCRF—Policy charging rules functionPDCP—Packet Data Control ProtocolPDN—Packet data networkPGW—Packet data network gatewayPHY—PhysicalQCI—QoS class identifierQoS—Quality of serviceRAN—Radio access networkRLC—Radio link controlRLF—Reverse link failureRRC—Radio resource controllerRRM—Radio resource managementSC—SON collectorSGW—Serving gatewaySM—Service measurementSO—Self-optimizationSON—Self-organizing networkTA—Tracking areaTAU—Traffic area updatesTCP—Transmission Control ProtocolTEID—Tunnel endpoint identifierTFT—Traffic flow templateTSG—Technical Specification GroupTV—TelevisionUDP—User Datagram ProtocolUE—User equipmentUL—UplinkUMTS—Universal Mobile Telecommunications
SystemUTRAN—UMTS Terrestrial Radio Access
NetworkVoIP—Voice over IPWiMAX—Worldwide Interoperability for
Microwave Access
DOI: 10.1002/bltj Bell Labs Technical Journal 101
services including Web browsing, File Transfer
Protocol (FTP), video streaming, VoIP, online gaming,
real time video, push-to-talk, and push-to-view, as
well as a plethora of new mobile applications for
smartphones. Therefore, LTE is being designed as a
high data rate, low latency system.
These data services introduce demand fluctua-
tions that are intrinsically larger than those of tradi-
tional voice services. The multidimensional nature of
demand, its temporal dependence, and its increased
dynamic range render traditional optimization strate-
gies based on a peak (albeit composite) loading pro-
gressively less effective at efficiently allocating and
managing network resources.
LTE specifies a set of fast control algorithms that
aim to account for the dynamics introduced by varia-
tions in channel conditions and traffic loading
through scheduling that includes resource block allo-
cation, modulation and coding scheme (MCS) selec-
tion; multiple input multiple output (MIMO)
decisions; and handover. In addition to these
autonomous per-user equipment (UE)/per-bearer
controls at the radio access network (RAN), we need
autonomous aggregate controls, typically on two
dimensions:
1. Across the E-UTRAN and evolved packet core
(EPC) on a per-UE basis, and
2. Across a region of enhanced Node Bs (eNodeBs or
eNBs) over an aggregation of UEs.
These mechanisms also require the following
capabilities:
• State- and time-dependent control parameters to
help the network adapt the coverage and capacity
trade-off for multiple services in response to spatio-
temporal demand variations,
• Coordinated load balancing mechanisms that can
address demand and traffic fluctuations by opti-
mally “smoothing out” uncorrelated demand
peaks between neighboring cells and even
between differing wireless technologies, and
• Active measures to address rare but undesirable
events, such as reducing dropped and blocked
calls.
Such optimization solutions will translate into
benefits for service providers and mobile users such as
improved coverage; fewer reverse link failures (RLFs),
i.e., dropped connections; better QoS; and higher
throughput. Service providers will also benefit from
reduced maintenance and operation costs, the ability
to capitalize from higher network capacity, and
quicker launch of new services.
To further understand the requirement for the
control algorithms for optimization, let us look at it
from another perspective. In order to meet demand-
ing performance objectives, deployments of fourth
generation (4G) cellular technologies, specifically,
Long Term Evolution, will require the use of smaller
footprint cells than the norm currently found in sec-
ond generation (2G) and third generation (3G) net-
works. For realizing the importance of multi-vendor
operability and the economics of managing a greater
number of cells, 3GPP—the LTE standards body—
through its TSG-RAN working group has proposed a
set of capabilities known as self-organizing networks
(SON), which comprise self-configuration and self-
optimization [1–4]. Self-configuration aims to reduce
the cost of network setup, both during initial deploy-
ment and in the subsequent expansion phase. Self-
optimization (SO), on the other hand, aims to reduce
the operating expenditure (OPEX) cost by continu-
ously optimizing radio resource management (RRM)
parameters for load balancing, coverage, throughput,
and other parameters. However, deployment of SO
capability in the LTE networks may be optional, and
the equipment vendor may need to provide flexibility
in its software for the operators to selectively deploy
and control SO functionality at different parts of the
network as and when required.
Self-optimization processes have already been
studied in 2G and 3G networks, typically with the
objective of improving radio coverage and channel
capacity [5, 6, 8]. Though the initial 3GPP LTE rec-
ommendation for SON concentrates mainly on self-
optimization of radio resources at eUTRAN nodes, we
envisage an expansion of the scope to include higher
layer self-optimization of services and applications,
achievable by tuning available network resources to
allow optimum performance for each QoS class [5].
Flat IP networking architecture in LTE provides an
opportunity for flexible network monitoring and self-
optimization at the application level. Since IP appli-
cations can be characterized in terms of flows,
102 Bell Labs Technical Journal DOI: 10.1002/bltj
network monitoring systems can collect data on flows
and even at deep packet level. Having such a detailed
data collection and analysis will enable optimization at
the level of application profile and bearer classes
at different timescales.
In this paper, we propose a distributed, client-
based SON software architecture in which the data
collection clients reside at multiple levels of LTE
including eNodeBs in eUTRAN and at the mobility
management entity (MME), gateway nodes (serving
gateway [SGW], packet data network gateway
[PGW]) in the EPC. Similarly, the SON control mod-
ules will have their own hierarchy and are distributed
over different nodes. Though several of the use cases
proposed by 3GPP are mainly targeted towards auto-
installation and auto-configuration during both
greenfield deployment and network expansion
phases, we focus exclusively on the continuous self-
optimization part of SON functionalities. Moreover,
the current 3GPP proposal targets optimization at
local nodes, for example, handoff between adjacent
NodeBs, and does not have clear recommendations
for network level and/or region-wide optimization.
We propose a SON hierarchy based on the notion
of tracking areas, and/or user-defined regions of
cell clusters. Under our approach, pair-wise inter-cell
optimization can be viewed as a special case of our
SON hierarchy.
A comprehensive data collection mechanism for
SON should include an application profile, IP packet
and flow level details from the core network facing
nodes (e.g., PGW, SGW), and connection, session, and
coverage level details from the radio access side. In this
paper, we describe Celnet Xplorer, a well-developed
system that can be used to collect RRM related SON
data. An earlier version of this tool has been used to
monitor and analyze Code Division Multiple Access
(CDMA) 1X and Evolution Data Optimized (EV-DO)
networks [6, 8] and has since been evolved to the
currently emerging LTE network. We highlight Celnet
Xplorer’s capabilities that are particularly suitable for
SON requirements in terms of not only RRM related
data collection, but also its built-in statistical models
[7] for predicting trends of related SON key perfor-
mance indicator (KPI) metrics.
The rest of the paper is organized as follows. We
begin by describing the proposed distributed client
architecture for data collection and SON control. Next,
we discuss an example implementation of a dis-
tributed data collection and analysis system called
Celnet Xplorer for LTE (CX-LTE) and how this system
can be used to help realize SON functionalities. We
follow with a discussion around the grouping of cells
into tracking areas (TAs) for evolving a hierarchical
SON policy enforcement, and then describe additional
use-case scenarios for network-wide optimization.
SON Client-Server ArchitectureFor both data collection and SON control we pro-
pose a distributed client-server architecture as shown
in Figure 1. These software clients should be pro-
grammable to allow configuration for capturing, fil-
tering, and analyzing network performance data by
data type, time, or location within the network. The
clients can be programmed to respond to specific
anomalies, such as an increase in a key metric, and
collect relevant data according to a prescribed policy
for network optimization. These individual clients can
be configured and queried through a common net-
work interface and communicate only the necessary
information required by the user. In doing so, this
minimizes the processing, memory, and transmission
bandwidth in the backhaul network for any given net-
work optimization. Further, this information can be
made readily available to improve application perfor-
mance, which would require cross layer optimization
in the mobile network.
The proposed architecture consists of a union of
two complementary functional architectures: one for
data collection and the other for effecting SON con-
trol. This architecture relies on software clients that
reside anywhere along the network from the UE to
eNBs and the EPC nodes MME, SGW, and PGW.
There are two types of software clients: data collectors
(DCs) to collect data to monitor network performance
and its status and SON controllers (SCs) to enforce SON
functionalities. In order to provide an accurate view of
the current state of the network and consequently to
realize different SON functionalities, we need to col-
lect comprehensive data for call/session related events
DOI: 10.1002/bltj Bell Labs Technical Journal 103
at different levels: application layer, IP flows, IP pack-
ets, and the radio access layer. For SON purposes, data
collection refers to both protocol messages and some
data on/from payload packets. The data collection
clients collect different protocol messages and other
data depending on where they reside. For example,
DC at eNBs collects all per-connection related X2 mes-
sages, S1u, and radio resource controller (RRC)-
related events, while DC at the PGW collects data
pertaining to individual flows and payload packet
related statistics. The DC client at the UE collects data
pertaining to individual applications and user experi-
ences and sends it to the network via the DC client at
eNodeB to which it is attached. Collection of data per-
taining to different time granularity is another critical
need that is addressed by these DC clients. When volu-
minous data is continuously collected from hundreds of
nodes, it should be properly filtered, analyzed, and
stored for efficiency and longevity. This is achieved by
storing data at a centralized database server as shown
in Figure 1. The data stored at the centralized database
can then be analyzed by the SON engine at the opera-
tions, administration, and maintenance (OAM) center
to monitor SON-related parameters and to infer net-
work performance and efficiency. The SON engine ini-
tiates SON control actions, via SON controllers, based
on the inferred parameter values and the network con-
ditions. It should also be noted that the SON engine
may implement a rule-based inference system to
arrive at optimization decisions. In addition to storing
Data collection client
SON controller
OAM
SGW
Wide area IPnetwork
MME
eNBn
eNB1UE
SON_Cluster
DC
SC
PGW
SC
SC
SC
SC
DC
DC
DC
DC DC
SONengine
SON databaseKPI computation
SC
DC
Data collection pathSON control pathCommunication path
eNB—Enhanced NodeBIP—Internet ProtocolKPI—Key performance indicatorMME—Mobility management entityOAM—Operations, administration, and maintenance
PGW—Packet data network gatewaySGW—Serving gatewaySON—Self-organizing networkUE—User equipment
Figure 1.Distributed client-server architecture for SON data collection and control.
104 Bell Labs Technical Journal DOI: 10.1002/bltj
data, the database server can also perform addi-
tional computations, as and when requested,
and/or on a predefined schedule, for example, to
extract SON-related KPIs and their patterns. With
the centralized database server, it is possible to
embed statistical learning algorithms and models to
predict both the short term and long term trends
for SON parameters over different hierarchies of
the network. Thus, the SON engine at the OAM
center and SON controllers at various nodes com-
plete the SON loop. It should be noted that both
sub network and network-wide SON optimization
will provide robust results when compared to local
eNodeB based optimizations.
In this paper, our focus is on data collection and
analysis, especially on how to implement an efficient
data collection and analysis mechanism for SON func-
tionalities, as well as how this data can be used
to compute SON-specific KPIs and to implement sta-
tistical models to predict SON parameters. In the next
sections, we describe Celnet Xplorer, a data collection
and analysis system that can play an important role
in implementing the SON functionali-ties of LTE
networks.
Celnet Xplorer Architecture: An OverviewAs stated above, having the proper and timely
data available to a SON’s analysis engine is the key to
network optimization. Celnet Xplorer is a non-intrusive,
non-loading, and vendor independent monitoring
tool for the LTE network that provides real-time per-
formance statistics about various metrics of the
E-UTRAN and some of EPC. Celnet Xplorer has these
key monitoring, troubleshooting, and optimization
functions that make it suitable for dynamic optimiza-
tion of LTE networks:
1. Per-UE measurement. The capability to measure
aggregate and per-mobile information for all
connections/sessions within a region of cells
(MME/MME pool footprint), thereby generating
a complete picture of the system state, which
retains all correlations between the measured
variables. A secondary benefit is shorter total
measurement time to diagnose/examine a
concern.
2. Fine granularity. The ability to measure system
data upon timescales significantly finer than tra-
ditional service measurement (SM), ideally upon
the timescales of the phenomena under exami-
nation (i.e., from milliseconds to seconds).
3. Intelligent data aggregation. Aggregation of data, so
that rapid analyses of potentially large data sets
are readily possible and convenient.
4. Negligible load on network. Data collection opera-
tion with negligible impact upon a fully loaded
network.
5. Privacy retention. Per-user personal information,
with no examination of voice/data payload.
6. Vendor independent. Applicability to multi-vendor
network scenario (except for the Celnet Xplorer
client at eNodeB, which is available only at
Alcatel-Lucent eNodeBs).
To understand the Celnet Xplorer’s architecture, it
is essential to understand the architecture of the
underlying network. The currently agreed architec-
ture for LTE interworking with evolved high rate
packet data (eHRPD) is as shown in Figure 2.
The architecture consists of the following func-
tional elements:
• Evolved radio access network. The evolved RAN for
LTE consists of a single node, i.e., the eNodeB that
interfaces with the UE. The eNB hosts the physi-
cal (PHY), medium access control (MAC), radio
link control (RLC), and Packet Data Control
Protocol (PDCP) layers that include the function-
ality of user-plane header-compression and
encryption. It also offers radio resource control
functionality corresponding to the control plane.
It performs many functions including radio
resource management, admission control,
scheduling, enforcement of negotiated uplink
(UL) QoS, cell information broadcast, ciphering/
deciphering of user and control plane data, and
compression/decompression of downlink (DL)/UL
user plane packet headers.
• Serving gateway. The SGW routes/forwards user
data packets. It also acts as the mobility anchor for
the user plane during inter-eNB handovers and as
the anchor for mobility between LTE and other
3GPP technologies. It manages IP bearer service.
DOI: 10.1002/bltj Bell Labs Technical Journal 105
• Mobility management entity. The MME is the key
control-node for the LTE access network. It is
responsible for idle mode UE tracking and paging
procedures including retransmissions. It is
involved in the bearer activation/deactivation
process and is also responsible for choosing the
SGW for a UE at the initial attach and at time of
intra-LTE handover involving core network (CN)
node relocation. It is responsible for authenticat-
ing the user by interacting with the home sub-
scriber server (HSS). The non-access stratum
(NAS) signaling terminates at the MME and is
also responsible for generation and allocation of
temporary identities to UEs. The MME also ter-
minates the S6a interface towards the home HSS
for roaming UEs.
• Packet data network gateway. The PGW provides
connectivity from the UE to external packet data
networks by being the point of exit and entry of
traffic for the UE. A UE may have simultaneous
connectivity with more than one PGW for access-
ing multiple PDNs. The PGW performs policy
enforcement, packet filtering for each user, charg-
ing support, lawful interception, and packet
screening. Another key role of the PGW is to act
as the anchor for mobility between 3GPP and
3GPP—3rd Generation Partnership Project3GPP2—3rd Generation Partnership Project 2AAA—Authorization, authentication, and accountingAN—Access networkBTS—Base transceiver stationeAN—Evolved access networkeHRPD—Evolved HRPDeNodeB—Enhanced NodeB
EPS—Evolved Packet SystemE-UTRAN—Evolved UTRANHRPD—High rate packet dataHSGW—HRPD serving gatewayHSS—Home subscriber serverIMS—IP Multimedia SubsystemIP—Internet ProtocolISDN—Integrated services digital networkMME—Mobile management entity
PCF—Packet control functionPCRF—Policy charging rules functionPDN—Packet data networkPSS—PSTN/ISDN simulation subsystemPSTN—Public switched telephone networkUMTS—Universal Mobile Telecommunications SystemUTRAN—UMTS Terrestrial Radio Access Network
eNodeB
MME
Servinggateway
PDNgateway
HSS
AN-AAA
HRPD BTS
eAN/PCF
HSGW
PCRF
3GPP AAAserver
3GPP2AAA server
Operator’s IPservices (e.g., IMS,
PSS)
S6a
S7S7c
Rx*
Wx*
S11
S6c
SGi
S7a
S103-US101
S1-u
S1-MME
A10/A11
S2a
AAA
Pi
S10
A13/A16
Ta*
X2E-UTRAN/
EPC
eHRPD
Figure 2.Non-roaming architecture for 3GPP � eHRPD access.
106 Bell Labs Technical Journal DOI: 10.1002/bltj
non-3GPP technologies such as Worldwide
Interoperability for Microwave Access (WiMAX)
and 3GPP2 (CDMA 1X and EV-DO).
• HSGW. The high rate packet data (HRPD) serving
gateway, or HSGW, provides interworking
between the HRPD (EV-DO) access node and the
packet data network gateway.
• Evolved radio network controller (eRNC) and eHRPD.
eRNC is a 3GPP2 RNC (EV-DO) that is capable of
interworking with the LTE network. eHRPD refers
to the HRPD network consisting of the eRNC,
HSGW, packet control function (PCF), and other
network nodes.
Celnet Xplorer non-intrusively monitors the fol-
lowing interfaces between the above mentioned net-
work elements of the LTE-eHRPD network:
• S1-MME. Reference point for the control plane
protocol between E-UTRAN and MME. Non
access stratum messages between the UE and the
MME are embedded within the S1-MME mes-
sages. By monitoring the S1-MME messages,
Celnet Xplorer records context setup and release
events, dedicated bearer setup and release events,
X2-based path switch events, S1-based handover
events, and OAM events. By monitoring the NAS
messages, Celnet Xplorer records the EMM and
ESM events such as attach, detach, service
request, dedicated bearer setup, identification,
and security mode.
• S1-U. Reference point between E-UTRAN and
serving GW for the per-bearer user plane tunneling
and inter eNodeB path switching during handover.
Basic monitoring of this interface will provide
bearer level packet (traffic) statistics. Deep packet
inspection to decode the Transmission Control
Protocol (TCP)/IP (or User Datagram Protocol
[UDP]/IP) and layers above provides applica-
tion level statistics and other information.
• S6a. This interface is defined between MME and HSS
for authentication and authorization. Monitoring
this interface provides details about location man-
agement procedures, subscriber data handling
procedures, and authentication procedures.
• S10. This interface is a reference point between
MMEs for MME relocation and MME-to-MME
information transfer. Celnet Xplorer monitors this
interface to gain details on UE context transfer
from old MMEs to new MMEs as a result of the
UE doing an “attach” at the new MME. In addi-
tion to this, Celnet also monitors the transaction
between the old and the new MMEs regarding
UE identification when it does a tracking area
update from a new MME.
• S11. This interface is a reference point between
the MME and serving GW. By monitoring this
interface, Celnet Xplorer records the create ses-
sion procedure (initiated from the MME), the cre-
ate dedicated bearer procedure (initiated from the
SGW), modification of bearer information such
as S1-U tunnel endpoint identifier (TEID) as a
result of new connection or path switch, QoS pro-
file modification, and deletion of a session or a
bearer.
• S101. This interface is the signaling interface
between the EPC MME and the evolved HRPD
access network (eAN/PCF). Messages related to
pre-registration of a hybrid UE (LTE and eHRPD
capable) with the eHRPD network as well as hand-
over of the UE from LTE to eHRPD network are
available in this interface.
Celnet Xplorer monitors the above mentioned
interfaces, decodes every packet, and builds a state
machine corresponding to each and every connection
and session on a per-UE basis. This includes correlating
the information across all these interfaces. Periodically
and at the end of every connection/session, essential
data from the UE’s connection and session are
uploaded to Celnet’s database. Key performance indi-
cators are generated from this database.
Events (such as failures) that take place at the
RRC/RLC/MAC layer are recorded at a client running
in the eNodeB on a per-UE/per-connection basis, and
then this information is transmitted to the MME over
the S1-MME interface using a private message. This
includes metrics such as retransmissions that occur at
the RLC layer, block error rate (BLER), channel quality
indicator (CQI) (computed here as average/maximum
[avg/max]), and X2 handover status and measure-
ment report. This record is also uploaded to the
database.
DOI: 10.1002/bltj Bell Labs Technical Journal 107
The most important feature of Celnet Xplorer is
that it retains the temporal correlation between
every event occurring in the connection and session.
Since the time stamp of every event is recorded, it is
possible to do real-time analysis or post analysis at
any granularity involving filtering/aggregation based
on any characteristic classifier of the calls. This facili-
tates the analysis of several of the connection/
session flow procedures mentioned in the 3GPP
23.401 standard.
Thus, Celnet Xplorer data collection and analysis
make the performance data available in a suitable
form for SON analysis, resulting in real-time tuning of
E-UTRAN and/or EPC configuration parameters.
Celnet Xplorer Measurement and ReportingCapabilities
Celnet directly monitors the S1-MME, NAS
(embedded within S1-MME), S10, S101, S11, and S6a
links and receives data from clients running
at the eNBs. From this data we are able to reconstruct
the events of each user within the LTE network. The
type of data available for report generation and SON
analysis is quite large and measured at the millisecond
timescale. Data is also aggregated on different timescales
(minutes to weeks) so not only are very short timescale
events recorded but longer term trends can also be ana-
lyzed and future trends predicted.
To get a general idea of what kind of reports/
analysis could be performed by Celnet and the use-
fulness of this to a SON engine, here are some examples
of measured and analyzed data.
For most of the captured data, the data/events
can be sorted, filtered, or binned on any combination
of the following:
• Time
• UE international mobile subscriber identity
(IMSI)/globally unique temporary identity (GUTI)
• Cell/eNB
• SGW
• PGW
• Access point name (APN)
Reports or analysis can be performed on almost any
event. Below are some representative events but by no
means all the events Celnet is capable of monitoring.
1. Attach requests or service requests
• Success or failure
• Failure causes (Data includes a complete list of
all failures and their time order. For example,
if an attach is rejected with an ESM cause, our
data prior to the attach indicates that the PDN
request was rejected with a reason equal to
network failure.)
• UE or network initiated
• Complete bearer information (Data includes
number and type. Further bearer analysis is
described below.)
• Handoff information (Number, type, latency,
success/failure)
• Latencies for most events (e.g., initial context
setup, modify bearer, handover, or authenti-
cation)
• Connection duration, bearer durations
• Context release cause
2. Bearer information
• Default or dedicated
• Set up, modify, drop times, latencies, and flow
durations
• QoS class identifier (QCI), preemption capa-
bility, vulnerability, allocation and retention
priority, charging chrematistics
• Guaranteed bit rate (GBR) and maximum bit
rate (MBR) for UL and DL
• Bytes passed, maximum and average through-
put per S1-U connection
• Packet filter details and packet filter analysis.
The throughput and port duration aggrega-
tion has to occur at the level of:
— Base IP address
— (UDP/TCP) port range
3. Handoff/path switch
• S1 or X2 based
• Source and target eNBs
• Latency
• Success or failure and failure reasons
(in detail)
• Event trigger for handoff
4. Paging
• Success or failure and failure reasons
• Number of pages
108 Bell Labs Technical Journal DOI: 10.1002/bltj
• Page types used
• Page attempt reason (bearer modification or
data available)
• Times and latencies
5. Traffic area updates (TAU)
• Type (periodic or event based)
• Success or failure and failure reasons
• Times and latencies
6. Latency (Note: this is a derived metric)
• Service request latency (initial context set up
S1AP � modify bearer S11)
• Attach latency
• Path switch latency
• Authentication latency (HSS and UE)
Table I provides a flavor of the correlated analy-
sis that can be carried out from the Celnet Xplorer
data.
In the EV-DO implementation of Celnet Xplorer,
the KPIs such as failed connection attempts, dropped
connections, session failures, and throughput were
correlated with the location coordinates of the mobile
device. The location information in EV-DO was
extracted from passive monitoring of the route update
reports. In the current specifications of LTE, the mea-
surement reports or any other report from the mobile
are not designed to contain enough information for
passive geolocation. Efforts are under way to make
location information available for passive geolocation,
and when these efforts succeed, Celnet Xplorer will be
able to geolocate a UE in real time and also provide
geolocation reports for the above mentioned KPIs.
This information will be extremely valuable for opti-
mization purposes.
From previous work with EV-DO networks and
Celnet Xplorer, we have developed the ability to pre-
dict the KPIs. This is a very desirable capability, especially
in the context of SONs. Many network measurements
(such as traffic counts or network delays) exhibit daily
or weekly cyclical patterns. At the same time, these
cyclical patterns change over time as circumstances
change from day to day. Building a baseline model
from this data is non-trivial due to the time-varying
nature of the expected normal behavior. In the EV-DO
implementation of Celnet Xplorer, we successfully
demonstrated an online monitoring methodology for
the time-varying cyclical streams of network data,
which combines a baseline state-space model and
statistical control schemes to monitor departures from
the baseline model. The state-space model character-
izes the normal evolution of the time series data, an
observation equation captures the daily/weekly pat-
terns using splines, and a state equation captures the
normal changes in the daily/weekly patterns.
Parameters of the state space models are initialized
based on the training data, and updated for each incom-
ing observation. The predicated values of the KPIs can
be used for applicable optimization strategies.
In the EV-DO implementation of Celnet Xplorer,
novel statistical control schemes for monitoring were
designed based on forecasting errors from the base-
line model, under the framework of statistical change
detection. Figure 3 provides an illustration of moni-
toring for the non-roaming architecture of 3GPP �
eHRPD access. The algorithm and results are dealt
with in detail in [9]. Figure 4 shows the time series
plot (black curve) of the number of attempted con-
nections (square root scale) for a base station in the
EV-DO network. The daily cycle is evident here, as
well as the weekday and weekend differences. Figure
4 also shows the resulting fit using the filtering algo-
rithm discussed in [9]. The one-step-ahead forecast
(prediction) of the square root counts from the base-
line model is shown in the figure as the middle
smoother curve, and the point-wise predictive confi-
dence intervals for the quantiles 0.01% and 0.99%
are shown as the top and bottom envelopes. As can be
seen, there is quite a lot that can be carried out by a
SON’s engine based on the Celnet Xplorer data. Many
different correlations and functions can be derived
and implemented in a feedback loop to continuously
tune certain parameters of a LTE network and receive
feedback on the effectiveness of tuning.
Celnet Xplorer Extension for SONFigure 5 illustrates the extended functional archi-
tecture of Celnet Xplorer for SON implementation.
In this extension we incorporate another module,
called CXx-SON, to perform two SON specific com-
putations: 1) SON-related KPI extraction and 2) sta-
tistical models to predict (trends of) SON KPIs.
Success/failure of: Correlation with respect to all or a subset of:
• UE attach
• Detach (UE/HSS/MME initiated)
• Tracking area updates, location area updates
• Service requests
• Initial context set up
• Paging
• Identification, GUTI reallocation, and other NAS processes
• Bearer activation, deactivation (UE and PGW initiated)
• Bearer modification (UE/PGW/HSS initiated)
• S6a procedures (insert subscriber data/purge/update location)
• RRC connection establishment/release (drops)
• Handover events:
• Intra-eNB, inter-eNB with and without MME/SGW change, LTE, HRPD
• Throughput per bearer (default and dedicated):
• Radio bearer throughput (avg and peak) based on reports from thin client in eNB
• S1-U bearer throughput at a finer granularity
DOI: 10.1002/bltj Bell Labs Technical Journal 109
The main motivation for this extension is to compute
SON KPIs as and when data arrive and hence relieve
the SON engine from additional computation bur-
dens, as well as to reduce the amount of queries that
the SON engine has to perform against the Celnet
database. Apart from KPI computations, we also incor-
porate statistical learning algorithms and models to
predict KPIs based on the historical data. For example,
we can predict whether a particular cell is about to
be overloaded based on its historical load patterns.
Similarly, we can predict whether there will be a
potential surge of high-bandwidth long-session ori-
ented connections based on the type of UEs that are
moving into an eNodeB. Thus, these models can act as
Table I. Celnet Xplorer LTE measurements and correlations.
APN—Access point nameavg—AverageBLER—Block error rateCQI—Channel quality indicatorDL—DownlinkDPI—Deep packet inspectioneBTS—Evolved base transceiver stationeNB—Enhanced nodeBeRNC—Evolved radio network controllerFreq—FrequentGUTI—Globally unique temporary identityHARQ—Hybrid automatic repeat requestHRPD—High rate packet dataHSS—Home subscriber serverIMSI—International mobile subscriber identity
LTE—Long Term EvolutionMIMO—Multiple input multiple outputMME—Mobile management entityNAS—Non-access stratumPGW—Packet data network gatewayQCI—QoS class identifierQoS—Quality of serviceRRC—Radio resource controllerRTD—Round trip delayRx—ReceiverSGW—Serving gatewaySINR—Signal-to-noise ratioTx—TransceiverUE—User equipmentUL—Uplink
• UE identity (IMSI/GUTI)
• Mobile make/model
• Cell/eNodeB/MME/SGW/PGW/APN/eRNC/eBTS association
• UE’s radio conditions:
— CQI (DL), SINR (UL)— HARQ, BLER— Power (Tx and Rx)— Scheduling delay— Non preferred freq zone incidents— MIMO decision
• UE’s position:
— RTD— Whether cell edge or not— Location (depends on LTE support for geolocation)
• eNodeB/MME loading
• UE buffer occupancy, power headroom, bearer characteristics (QCI)
• Bearer—type of application association (through a DPItool)
110 Bell Labs Technical Journal DOI: 10.1002/bltj
eNodeB
MME
Servinggateway
PDNgateway
HSS
AN-AAA
HRPD BTS
eAN/PCF
HSGW
PCRF
3GPP AAAserver
3GPP2AAA server
Operator’s IPservices (e.g., IMS,
PSS)
S6a
S7S7c
Rx*
Wx*
S11
S6c
SGi
S7a
S103-US101
S1-u
S1-MME
A10/A11
S2a
AAA
Pi
S10
A13/A16
Ta*
X2
Celnet Xplorerdata captureand analysis
Celnet client
E-UTRAN/EPC
eHRPD
3GPP—3rd Generation Partnership Project3GPP2—3rd Generation Partnership Project 2AAA—Authorization, authentication, and accountingAN—Access networkBTS—Base transceiver stationeAN—Evolved access networkeHRPD—Evolved HRPDeNodeB—Enhanced NodeB
EPS—Evolved Packet SystemE-UTRAN—Evolved UTRANHRPD—High rate packet dataHSGW—HRPD serving gatewayHSS—Home subscriber serverIMS—IP Multimedia SubsystemIP—Internet ProtocolISDN—Integrated services digital networkMME—Mobile management entity
PCF—Packet control functionPCRF—Policy charging rules functionPDN—Packet data networkPSS—PSTN/ISDN simulation subsystemPSTN—Public switched telephone networkUMTS—Universal Mobile Telecommunications SystemUTRAN—UMTS Terrestrial Radio Access Network
Figure 3.Monitoring using Celnet Xplorer for the non-roaming architecture of 3GPP � eHRPD access.
Jul 23 Jul 25 Jul 27 Jul 29 Jul 31 Aug 02 Aug 04
Count (square root
scale)
EV-DO—Evolution data optimized
The one-step-ahead forecast (prediction)
0.01% and 99.99% point-wise predictive confidence interval
Raw data in square root scale
12
8
4
0
Figure 4.Monitoring the number of attempted connections for a base station in an EV-DO network where the data isobserved every five minutes over a two week period.
DOI: 10.1002/bltj Bell Labs Technical Journal 111
a trigger for SON to initiate proactive optimization
steps.
SON Use CasesIn this section we suggest SON use-case scenarios.
The first use case discusses the need for tracking area
optimization and the input parameters for the SON
engine that is provided by Celnet Xplorer to carry this
out. The second use case discusses the dynamic recon-
figuration of bearer profile parameters by the SON
engine as a result of a) deep packet inspection (DPI) of
the applications running at the UE and b) Celnet
Xplorer’s forecast of traffic load at the eNodeBs. The
third use case illustrates tuning the overall network
for coverage and capacity based on the cell traffic and
different failure mechanisms. This tuning can be used
to account for shifts in traffic pattern, additions of
new cells, or inadequacies of the previous network
settings.
Tracking Area OptimizationFrom a mobility perspective, the UE can be in
one of three states, LTE_DETACHED, LTE_IDLE,
and LTE_ACTIVE. In the LTE_ACTIVE state, the UE
is registered with the network and has an RRC con-
nection with the eNB. In LTE_ACTIVE state, the net-
work knows the cell to which the UE belongs and can
transmit/ receive data from the UE. The LTE_IDLE
state is a power-conservation state for the UE, where
typically the UE is not transmitting or receiving pack-
ets. In LTE_IDLE state, the location of the UE is
known at the granularity of a tracking area that con-
sists of multiple eNBs.
To track user equipment, the mobility manage-
ment entity records the TA in which each user is reg-
istered. When a UE moves into a new TA, a tracking
area update message is sent to the MME. This TAU
procedure and the associated messaging contribute to
signaling overhead. To reduce this overhead, larger
tracking areas may be allocated. However, there is a
trade-off here with trying to reduce the paging over-
head.
When there is a UE-terminated call, MME broad-
casts a paging message to all the cells of the TA in
which the UE was last registered. When TAs are of
very small size, the number of pages required to suc-
cessfully reach the mobile is very low, but the number
of TAUs is very large, whereas very large TAs result in
a small number of TAU messages and large number of
paging messages. Thus a natural objective in TA plan-
ning is an optimal trade-off between the two types of
signaling overhead.
As user distribution and mobility patterns change
over time, tracking area configuration optimized for
user statistics (or forecasts) in the initial planning
phase will no longer perform well. For this reason,
TA design must be revised over time.
As input parameters to this optimization problem,
Celnet Xplorer provides the following performance
statistics:
• Connection setup failures due to paging,
• Number of paging attempts per connection for UE
terminated connections,
• Type of paging attempts that were successful (last
seen eNB, TA, or TA plus neighbors),
• Time between last connection and present page,
• Accuracy of the last seen tracking area observed
during paging,
OAM
CX-LTE
SON controlactions
DB
SON engine
CX-SON:KPI extraction
prediction modelsLTE network
Data collection/pre-analysis Data packets
CX—Cellnet XplorerDB—DatabaseKPI—Key performance indicatorLTE—Long Term EvolutionOAM—Operations, administration, and maintenanceSON—Self-organizing network
Data analysis/report
Figure 5.Extended functional architecture of Celnet Xplorer forSON.
112 Bell Labs Technical Journal DOI: 10.1002/bltj
• TAU density on a per-TA-neighbor TA basis and
also on a cell-neighbor cell basis when mobile
devices are at the border of the TA, and
• Number of TAUs, and the eNB and users impacted
by the TAU.
Note that a global view of the TAU and paging statis-
tics is required for stable optimization. Incorporating
time of day and day of week patterns would further
strengthen the algorithm.
Application-Based Automatic Bearer AssignmentFigure 6 provides an illustration of application-
based bearer assignment. In this use case, we consider a
smartphone or a personal computer (PC) card that ini-
tiates a video or VoIP call. The UE’s packets are carried
over the S1-U interface from the eNodeB to the SGW.
The SON capture module does a deep packet inspec-
tion and determines that the packet type is VoIP or
video, and that it is carried over a best effort (BE)
bearer. Ideally, one would expect the packets to be
carried automatically over guaranteed bit rate bearers.
However, inadequate provisioning at the EPC (at the
policy charging rules function [PCRF] in particular)
because of the complexity of keeping track of the
numerous third party applications on the smartphone
or the PC prevents these packets from being assigned
a GBR bearer.
It would be very valuable to the service provider
and to the end user for such applications to be
detected automatically by DPI at the network’s edge
4. Create dedicated bearer request
MME Serving GW PDN-GW PCRF
6. RRC connection reconfiguration
3. Create dedicated bearer request
7. RRC connection reconfiguration complete
8. E-RAB setup response
eNodeBUE
12. Create dedicated bearer response
9. Direct transfer (activate dedicated EPS bearer context accept)
UE application’s VoIP/video packets on BE bearer
MonitorSON capture &
detection
SON analysis &trigger
2. PCRF initiated IP-CAN session modification
1. Upgrade VoIP/video flowrequest & application flow details
5. E-RAB setup requestActivate dedicated EPS bearer context request
10. Uplink NAS (activate dedicated EPS bearer context accept)
11. Create dedicated bearer response
13. PCRF initiated IP-CAN session modification end14. Upgrade response
BE—Best effortEPS—Evolved Packet SystemE-RAB—E-UTRAN radio access bearerGW—GatewayIP—Internet Protocol
IP-CAN—IP Continental Area NetworkMME—Mobility management entityNAS—Non-access stratumPCRF—Policy charging rules functionPDN—Packet data network
RRC—Radio resource controllerSON—Self-organizing networkUE—User equipmentVoIP—Voice over IP
Figure 6.Application-based bearer assignment.
DOI: 10.1002/bltj Bell Labs Technical Journal 113
and then assigned to appropriate bearers. An SON
capture and analysis module is well suited for this
detection. In addition, this requires the SON analysis
module to query the PCRF/HSS in order to make sure
that the user has a subscription to GBR bearers. In a
case where a new subscription or surcharge for this
service is required, the user should be prompted for
the purchase. The SON capture and analysis module
also keeps track of the eNodeB’s loading conditions
so as not to overload the cell with requests for GBR
bearers when a BE bearer was used. When the SON
capture and analysis module knows that the eNodeB
can handle specialty bearers with QCI � 2 for GBR-
VoIP or QCI � 3 for conversational packet switched
video, it triggers the PCRF such that the PCRF sends a
PCC decision provision (QoS policy) message to the
PGW to create a new dedicated bearer with a corre-
sponding QoS policy for this application.
The PGW uses this QoS policy to assign the
Evolved Packet System (EPS) bearer QoS: i.e., it
assigns the values to the bearer level QoS parameters
QCI, allocation and retention priority (ARP), GBR,
and MBR. The PGW sends a create dedicated bearer
request message, including the EPS bearer QoS,
traffic flow template (TFT), and protocol configura-
tion options, to the serving GW. Protocol configuration
options can be used to transfer application level
parameters between the UE and the PGW. The serving
gateway sends the create dedicated bearer request
message to the MME.
The MME selects an EPS bearer identity, which
has not yet been assigned to the UE, and builds an
activate dedicated EPS bearer context request NAS
message including the TFT, EPS bearer QoS parame-
ters, protocol configuration options, and the EPS
bearer identity. The MME then signals the E-UTRAN
radio access bearer (E-RAB) setup request with
the EPS bearer identity and EPS bearer QoS to the
eNodeB.
Since the eNodeB has the resources available, it
acknowledges the bearer activation to the MME with
an E-RAB setup response message. The UE NAS layer
builds an activate dedicated EPS bearer context accept
message including EPS bearer identity. The UE then
sends a direct transfer RRC message to the eNodeB
with this NAS message embedded. The eNodeB sends
an uplink NAS transport message containing this NAS
message to the MME.
Upon reception of the response message from the
eNodeB as well as from the UE, the MME acknowl-
edges the bearer activation to the serving GW by
sending a create dedicated bearer response message.
The serving GW acknowledges the bearer activation
to the PGW by sending a create dedicated bearer
response message.
The PGW indicates to the PCRF whether the
requested PCC decision (QoS policy) could be
enforced or not, allowing the completion of the PCRF-
initiated IP-connectivity access network (CAN) ses-
sion modification procedure after the completion of
IP-CAN bearer signalling.
This completes the automatic detection and pro-
visioning of appropriate bearers for the VoIP/video
calls from a third party application running on top of
a smartphone or PC card.
Network-Wide OptimizationWireless networks as a whole are complex and
multiply coupled structures. Sometimes a local change
in one cell can cause problems in a previously untrou-
bled cell. Care must be taken when making local
changes not to disrupt the adjacent cells. Sometimes
it is also advantageous to look at the network as a
whole or on a bigger scale than just a few cell clusters.
This network-wide view will necessitate a centralized
data collection entity like Celnet Xplorer that can look
at short as well as long term trends over the entire
network.
The idea for a network-wide or very large cluster
optimization would be to collect data on cell load,
handoff rates, failure rates of attach, and service
request and other similar parameters and feed this
data into a SON engine similar to another tool devel-
oped at Bell Labs, called Ocelot. This SON-type engine
would have a model of the network topology (which
could also be updated by feedback from the Celnet
tool) that can be simulated, and then the network
parameters (such as antenna tilt, azimuth, and output
transmit power) can be optimized. Optimization
in this model trades off coverage and capacity to obtain
114 Bell Labs Technical Journal DOI: 10.1002/bltj
the best overall mix for the network goals (reduced
drops and blocks versus increased throughput).
In order for a SON engine such as Ocelot to work
properly, the network topology model input to it must
be modeled fairly accurately for the network layout.
Accurate information around traffic density and the
position of the failures is necessary. Celnet Xplorer
has shown in 3G1X and EV-DO networks that it can
provide maps of failure locations and traffic density.
Figure 7 shows the density of lost calls from a 3G1X
network as reported by Celnet Xplorer. The cells with
circles around them were the Celnet-monitored cells.
The squares with darker shades of gray show areas of
increased lost cells.
As can be seen, Celnet can provide failure loca-
tions and traffic density locations to feed into a model
such as an Ocelot-type SON engine. This data can be
used to tune the model, which will then lead to accu-
rate optimizations. The optimization can be run for
small cell clusters and/or scaled up to the entire net-
work. The important aspect of this type of optimiza-
tion is that the SON engine looks over a larger area
of the network so if the optimization is on a smaller
cluster of cells, the tool understands and models how
the changes to this small cluster impact the entire net-
work. This optimization, as stated before, could be
used to retune the network for cell additions or traf-
fic patterns for different times of the day. For example,
we may wish to have one network setting during the
workday versus one for the evening hours, as well as
different settings for the weekend. At present, the goal
is to drive toward a few optimized network settings
per day rather than to continually optimize the net-
work based on feedback to the SON engine. As the
model is proved in, optimized network changes can be
made based on more timely feedback.
There are several hurdles to overcome for this
Ocelot-based SON engine to become a reality.
Presently the most powerful optimization tuning
knobs—antenna tilt and azimuth—are generally not
available for remote optimization. Ideally in the
future, these parameters will become available to tune
the network. In addition, as stated above, more work
is needed on the geolocation aspects of some of the
UE-reported measures so that geolocation can be per-
formed more accurately.
3G—Third generation
Figure 7.Celnet Xplorer-generated traffic density map of lost calls for a 3G1X network gather for a 12 hour periodaggregated into 250 meter bins. Monitored cells are circled. Darker shades of gray/black indicate a higher numberof lost calls for that grid.
DOI: 10.1002/bltj Bell Labs Technical Journal 115
ConclusionOur paper describes a new software technology
for performance measurement that will be integral
for self-optimization in LTE mobile networks. The
software architecture provides a flexible and efficient
method of obtaining and analyzing critical perfor-
mance data regarding network and services opera-
tion and end user experience. We provide a
framework for utilization of this analysis by addi-
tional self-optimization and policy algorithms which
will allow a broad range of self-optimization strategies
to be implemented within these mobile networks.
The client-based architecture is scalable and mini-
mizes the processing and storage impact on the LTE
network elements. It provides real time measurement
and analysis for critical parameters of multimedia
applications and new terminal specific applications.
The potential for self-optimization in LTE net-
works offers not only the long-promised reduction in
operating costs, but the efficient management of a
plethora of new mobile applications for 4G networks.
These new applications combined with smartphones
such as the Apple iPhone* [10] and Android* [9]
based terminals will likely create the biggest challenge
for mobile network operators since the introduction of
data services in 3G networks some years back. Self-
optimization could alleviate some of the expense and
uncertainty with new market offers and accelerate
subscriber rates for these new services. Self-optimization
could extend the capabilities in LTE to best accom-
modate the technology demands of running the tens
of thousands of different mobile applications that are
viewed as the next tech industry wave [10].
AcknowledgementsWe acknowledge Kenneth Del Signore for pro-
viding valuable insights into some of the issues related
to paging load optimization. We also thank Aiyou
Chen and Jin Cao for their contribution towards sta-
tistical models implemented in the EV-DO version of
Celnet Xplorer and their suggestions for LTE imple-
mentation.
*TrademarksAndroid is a trademark of Google, Inc.Apple and iPhone are registered trademarks of Apple
Computer, Inc.
3G—Third generation
(a) Measured mobile density from7:20 to 7:30 pm
(b) Measured mobile density from7:30 to 7:40 pm
Figure 8.Traffic density variations as measured by Celnet Xplorer for a 3G1X network. Substantial traffic density variationsappear even on intermediate timescales in this four cell cluster.
116 Bell Labs Technical Journal DOI: 10.1002/bltj
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(Manuscript approved May 2010)
ARUMUGAM BUVANESWARI is a research engineer inthe End-to-End Wireless NetworkingDepartment at Alcatel-Lucent Bell Labs inMurray Hill, New Jersey. She holds a B.E.degree in electronics and communicationfrom Thiagarajar College of Engineering,
Madurai, India, and a master of science degree inelectrical communication engineering from the IndianInstitute of Science, Bangalore, India. During hertenure at Bell Labs, she has focused on data analysisand optimization of 3G1X, EV-DO, and LTE networks.She is the co-inventor of the Celnet Xplorer tool, andshe played a lead role in its productization. Herresearch interests are in optimization of radio accessand core networks through real network data, 4Gwireless systems, and embedded systems. She has anumber of publications in the areas of root causeanalysis of radio network failures, dynamicoptimization of radio networks, statisticalrepresentation of wireless calls, and digital signalprocessing algorithms and firmware.
LAWRENCE DRABECK is a research engineer at Alcatel-Lucent Bell Labs in Holmdel, NewJersey. He joined Bell Labs after completinghis Ph.D. in physics at the University ofCalifornia Los Angeles. His initial work wasfocused on radio frequency (RF) properties
and potential wireless applications of high-temperature superconductors. He has also worked onnext-generation radio front ends, interferencemodeling, and smart antennas. He is now part of theBell Labs E2E Wireless Networking Group, where heworks on real time network monitoring andoptimization.
NACHI NITHI is a member of technical staff in the Mathematics of Networks andCommunications Research Department atAlcatel-Lucent Bell Labs in Murray Hill, New Jersey. He earned a B.E. (honors) inelectrical engineering from Madras
University, Chennai, India; an M.E. in computer sciencefrom Anna University, Chennai, India; and a Ph.D. incomputer science from Colorado State University, FortCollins. He is a member of IEEE. He has published
DOI: 10.1002/bltj Bell Labs Technical Journal 117
papers in leading journals and conferences and holdsseveral patents. His main interests are in tools foroptical network design, switching center design, and3G and 4G wireless network monitoring; systemsimulations; and self-optimization networkapplications.
MARK HANER is a research manager in the Networking and Network Management ResearchDomain at Alcatel-Lucent Bell Labs inMurray Hill, New Jersey. He holds B.S., M.S.,and Ph.D. degrees in electrical engineeringand physics from the University of California
at Berkeley, where he also held a Miller ResearchInstitute fellowship. Dr. Haner has focused his researchactivities on broadband access and fixed and mobilewireless systems. His current interest is in network andapplication performance in 3G and 4G mobile networkssuch as LTE. He has served on advisory committees forboth DARPA and NSF.
PAUL POLAKOS is a director in the Networking and Network Management Research Domain atAlcatel-Lucent Bell Labs. He is currentlybased in Nozay, France. His focus at BellLabs is physics and wireless research. He hasbeen instrumental in the definition and
development of key technology initiatives for digitalwireless systems, including intelligent antennas (IA) andthe multiple input multiple output (MIMO) Bell LabsLayered Space-Time (BLAST) advanced base station andradio access network architectures; radio signalprocessing; enhancements to wireless networks forhigh data rates and high capacity; and dynamicnetwork optimization. He holds B.S., M.S., and Ph.D.degrees in physics from Rensselear Polytechnic Institutein Troy, New York, and the University of Arizona inTucson. Prior to joining Alcatel-Lucent, he was activelyinvolved in elementary particle physics research at theU.S. Department of Energy’s Fermilab and at theEuropean Organization for Nuclear Research (CERN)and was on the staff of the Max Planck Institute forPhysics and Astrophysics in Munich. He is author orcoauthor of more than 50 publications and holdsnumerous patents.
CHITRA SAWKAR is a member of technical staff in the E2E Wireless Networks ResearchDepartment at Alcatel-Lucent Bell Labs inMurray Hill, New Jersey. She received herbachelor’s degree in electrical engineeringfrom the University of Madras in Chennai,
India, and M.S. in electrical and computer engineering
from Rutgers University in New Jersey. While in LucentTechnologies’ Mobility Division, one of the manyactivities in which she was involved was the systemmodeling of the UMTS baseband processor, OneChip.Currently, she is working on the Celnet Xplorer, a highspeed network performance monitoring tool. Herresearch interests include mobile core networkevolution to efficiently manage the traffic explosionand managing network resources to deliver services tothe end user at an exceptional level of quality ofservice. ◆
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