Selfoptimization of LTE networks utilizing Celnet Xplorer

◆ 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,


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


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.


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.


• 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.


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.


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


• Number of pages


• Page types used

• Page attempt reason (bearer modification or

data available)

• Times and latencies

5. Traffic area updates (TAU)

• Type (periodic or event based)


• 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


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)


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.


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.


• 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.


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


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.


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.


References[1] 3rd Generation Partnership Project, “Self-

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[8] L. M. Drabeck, M. J. Flanagan, J. Srinivasan,W. M. MacDonald, G. Hampel, and A. Diaz,“Network Optimization Trials of a Vendor-Independent Methodology Using the Ocelot®

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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


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. ◆

Documents

Selfoptimization of LTE networks utilizing Celnet Xplorer