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Self-Optimization of LTE Networks Utilizing Celnet Xplorer Arumugam 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, autonomic self-optimization and configuration processes. Self-optimization processes have already been studied in second generation (2G) and third generation (3G) networks, typically with the objective of improving radio coverage and channel capacity. The 3rd Generation Partnership Project (3GPP) standard for LTE self-organization of networks (SON) provides guidelines on selfconfiguration of physical cell ID and neighbor relation function and selfoptimization for mobility robustness, load balancing, and inter-cell interference reduction. While these are very important from an optimization perspective of local phenomenon (i.e., the eNodeB’s interaction with its neighbors), it is also essential to architect control algorithms to optimize the network as a whole. In this paper, we propose a Celnet Xplorer-based SON architecture that allows detailed analysis of network performance combined with a SON control engine to optimize the LTE network. The network performance data is obtained in two stages. In the first stage, data is acquired through intelligent non-intrusive monitoring of the standard interfaces of the Evolved UMTS Terrestrial Radio Access Network (E-UTRAN) and Evolved Packet Core (EPC), coupled with reports from a software client running in the eNodeBs. In the second stage, powerful data analysis is performed on this data, which is then utilized as input for the SON engine. Use cases involving tracking area optimization, dynamic bearer profile reconfiguration, and tuning of network-wide coverage and capacity parameters are presented. © 2010 Alcatel-Lucent.

Introduction The 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

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.

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

Panel 1. Abbreviations, Acronyms, and Terms 2G—Second generation 3G—Third generation 3GPP—3rd Generation Partnership Project 4G—Fourth generation APN—Access point name ARP—Allocation and retention priority BE—Best effort BLER—Block error rate CAN—Connectivity access network CDMA—Code Division Multiple Access CN—Core network CQI—Channel quality indicator CX—Celnet Xplorer DC—Data collectors DL—Downlink DPI—Deep packet inspection eAN—Evolved access network eHRPD—Evolved HRPD EMM—Evolved mobility management eNB—Enhanced node B EPC—Evolved Packet Core EPS—Evolved Packet System E-RAB—E-UTRAN radio access bearer eRNC—Enhanced radio network controller ESM—Evolved Session Management E-UTRA—Evolved UMTS Terrestrial Radio Access E-UTRAN—Evolved UMTS Terrestrial Radio Access Network EV-DO—Evolution Data Optimized FTP—File Transfer Protocol GBR—Guaranteed bit rate GUTI—Globally unique temporary identity GW—Gateway HRPD—High rate packet data HSGW—HRPD serving gateway HSS—Home subscriber server IMSI—International mobile subscriber identity IP—Internet Protocol KPI—Key performance indicator LTE—Long Term Evolution MAC—Medium access control MBR—Maximum bit rate MCS—Modulation and coding scheme

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 traffic with end-to-end quality of service (QoS). Voice 100

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MIMO—Multiple input multiple output MME—Mobility management entity MMOG—Multimedia online gaming NAS—Non-access stratum OAM—Operations, administration, and maintenance OPEX—Operational expenditures PC—Personal computer PCF—Packet control function PCRF—Policy charging rules function PDCP—Packet Data Control Protocol PDN—Packet data network PGW—Packet data network gateway PHY—Physical QCI—QoS class identifier QoS—Quality of service RAN—Radio access network RLC—Radio link control RLF—Reverse link failure RRC—Radio resource controller RRM—Radio resource management SC—SON collector SGW—Serving gateway SM—Service measurement SO—Self-optimization SON—Self-organizing network TA—Tracking area TAU—Traffic area updates TCP—Transmission Control Protocol TEID—Tunnel endpoint identifier TFT—Traffic flow template TSG—Technical Specification Group TV—Television UDP—User Datagram Protocol UE—User equipment UL—Uplink UMTS—Universal Mobile Telecommunications System UTRAN—UMTS Terrestrial Radio Access Network VoIP—Voice over IP WiMAX—Worldwide Interoperability for Microwave Access

traffic will be supported mainly as Voice over IP (VoIP), enabling better integration with other multimedia services. Evolved UMTS terrestrial radio access (E-UTRA) is expected to support different types of

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 fluctuations that are intrinsically larger than those of traditional voice services. The multidimensional nature of demand, its temporal dependence, and its increased dynamic range render traditional optimization strategies based on a peak (albeit composite) loading progressively 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 variations in channel conditions and traffic loading through scheduling that includes resource block allocation, modulation and coding scheme (MCS) selection; 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 spatiotemporal demand variations, • Coordinated load balancing mechanisms that can address demand and traffic fluctuations by optimally “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 demanding 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 second generation (2G) and third generation (3G) networks. 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 selfoptimization [1–4]. Self-configuration aims to reduce the cost of network setup, both during initial deployment and in the subsequent expansion phase. Selfoptimization (SO), on the other hand, aims to reduce the operating expenditure (OPEX) cost by continuously 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 recommendation for SON concentrates mainly on selfoptimization 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 selfoptimization at the application level. Since IP applications can be characterized in terms of flows, DOI: 10.1002/bltj

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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, clientbased 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 modules will have their own hierarchy and are distributed over different nodes. Though several of the use cases proposed by 3GPP are mainly targeted towards autoinstallation and auto-configuration during both greenfield deployment and network expansion phases, we focus exclusively on the continuous selfoptimization 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 performance indicator (KPI) metrics.

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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 distributed 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 Architecture For both data collection and SON control we propose a distributed client-server architecture as shown in Figure 1. These software clients should be programmable to allow configuration for capturing, filtering, 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 network 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 network optimization. Further, this information can be made readily available to improve application performance, 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 control. 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 collect comprehensive data for call/session related events

OAM SON database KPI computation

DC UE

DC eNB1 SC

SON engine

DC

DC SGW

MME SC

SC DC PGW

DC SC

eNBn

Wide area IP network

SC SC

SON controller

DC Data collection client

SON_Cluster

Data collection path SON control path Communication path eNB—Enhanced NodeB IP—Internet Protocol KPI—Key performance indicator MME—Mobility management entity OAM—Operations, administration, and maintenance

PGW—Packet data network gateway SGW—Serving gateway SON—Self-organizing network UE—User equipment

Figure 1. Distributed client-server architecture for SON data collection and control.

at different levels: application layer, IP flows, IP packets, 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 messages, 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 experiences and sends it to the network via the DC client at eNodeB to which it is attached. Collection of data pertaining to different time granularity is another critical

need that is addressed by these DC clients. When voluminous 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 operations, administration, and maintenance (OAM) center to monitor SON-related parameters and to infer network performance and efficiency. The SON engine initiates SON control actions, via SON controllers, based on the inferred parameter values and the network conditions. 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

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data, the database server can also perform additional 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 complete 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 functionalities, as well as how this data can be used to compute SON-specific KPIs and to implement statistical 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 Overview As 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 performance 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 optimization 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.

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2. Fine granularity. The ability to measure system data upon timescales significantly finer than traditional service measurement (SM), ideally upon the timescales of the phenomena under examination (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 operation 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 architecture for LTE interworking with evolved high rate packet data (eHRPD) is as shown in Figure 2. The architecture consists of the following functional 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 physical (PHY), medium access control (MAC), radio link control (RLC), and Packet Data Control Protocol (PDCP) layers that include the functionality 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.

HSS Wx*

S6a PCRF

S10 S7c

MME

eNodeB

E-UTRAN/ EPC

X2

S7

S11

S1-MME

S1-u

S101

Rx*

Serving gateway

S7a SGi

PDN gateway

S103-U

Operator’s IP services (e.g., IMS, PSS)

S6c

S2a

3GPP AAA server Ta*

eHRPD HSGW Pi

3GPP2 AAA server

A10/A11 eAN/PCF

AN-AAA AAA

A13/A16 HRPD BTS 3GPP—3rd Generation Partnership Project 3GPP2—3rd Generation Partnership Project 2 AAA—Authorization, authentication, and accounting AN—Access network BTS—Base transceiver station eAN—Evolved access network eHRPD—Evolved HRPD eNodeB—Enhanced NodeB

EPS—Evolved Packet System E-UTRAN—Evolved UTRAN HRPD—High rate packet data HSGW—HRPD serving gateway HSS—Home subscriber server IMS—IP Multimedia Subsystem IP—Internet Protocol ISDN—Integrated services digital network MME—Mobile management entity

PCF—Packet control function PCRF—Policy charging rules function PDN—Packet data network PSS—PSTN/ISDN simulation subsystem PSTN—Public switched telephone network UMTS—Universal Mobile Telecommunications System UTRAN—UMTS Terrestrial Radio Access Network

Figure 2. Non-roaming architecture for 3GPP ⴙ eHRPD access.

•

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 authenticating the user by interacting with the home subscriber 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 terminates 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 accessing multiple PDNs. The PGW performs policy enforcement, packet filtering for each user, charging support, lawful interception, and packet screening. Another key role of the PGW is to act as the anchor for mobility between 3GPP and

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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 following interfaces between the above mentioned network 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 messages. 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 application 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 management procedures, subscriber data handling procedures, and authentication procedures. • S10. This interface is a reference point between MMEs for MME relocation and MME-to-MME

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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 addition 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 session procedure (initiated from the MME), the create 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 profile 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 handover 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 indicators 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 measurement 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 facilitates 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 Reporting Capabilities 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 analyzed and future trends predicted. To get a general idea of what kind of reports/ analysis could be performed by Celnet and the usefulness 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 authentication) • 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 capability, 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 throughput per S1-U connection • Packet filter details and packet filter analysis. The throughput and port duration aggregation 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

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• •

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 analysis 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 measurement 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 optimization purposes. From previous work with EV-DO networks and Celnet Xplorer, we have developed the ability to predict 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

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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 characterizes the normal evolution of the time series data, an observation equation captures the daily/weekly patterns 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 incoming 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 baseline model, under the framework of statistical change detection. Figure 3 provides an illustration of monitoring 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 connections (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 algorithm discussed in [9]. The one-step-ahead forecast (prediction) of the square root counts from the baseline model is shown in the figure as the middle smoother curve, and the point-wise predictive confidence 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 SON Figure 5 illustrates the extended functional architecture of Celnet Xplorer for SON implementation. In this extension we incorporate another module, called CXx-SON, to perform two SON specific computations: 1) SON-related KPI extraction and 2) statistical models to predict (trends of) SON KPIs.

Table I.

Celnet Xplorer LTE measurements and correlations.

Success/failure of:

Correlation with respect to all or a subset of:

• UE attach

• UE identity (IMSI/GUTI)

• Detach (UE/HSS/MME initiated)

• Mobile make/model

• Tracking area updates, location area updates

• Cell/eNodeB/MME/SGW/PGW/APN/eRNC/eBTS association

• Service requests

• UE’s radio conditions:

• Initial context set up

— — — — — —

• Paging • Identification, GUTI reallocation, and other NAS processes • Bearer activation, deactivation (UE and PGW initiated)

• UE’s position: — RTD — Whether cell edge or not — Location (depends on LTE support for geolocation)

• Bearer modification (UE/PGW/HSS initiated) • S6a procedures (insert subscriber data/purge/ update location) • RRC connection establishment/release (drops)

CQI (DL), SINR (UL) HARQ, BLER Power (Tx and Rx) Scheduling delay Non preferred freq zone incidents MIMO decision

• eNodeB/MME loading

• Handover events:

• UE buffer occupancy, power headroom, bearer characteristics (QCI)

• Intra-eNB, inter-eNB with and without MME/SGW change, LTE, HRPD

• Bearer—type of application association (through a DPI tool)

• 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 APN—Access point name avg—Average BLER—Block error rate CQI—Channel quality indicator DL—Downlink DPI—Deep packet inspection eBTS—Evolved base transceiver station eNB—Enhanced nodeB eRNC—Evolved radio network controller Freq—Frequent GUTI—Globally unique temporary identity HARQ—Hybrid automatic repeat request HRPD—High rate packet data HSS—Home subscriber server IMSI—International mobile subscriber identity

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 burdens, 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 incorporate statistical learning algorithms and models to

LTE—Long Term Evolution MIMO—Multiple input multiple output MME—Mobile management entity NAS—Non-access stratum PGW—Packet data network gateway QCI—QoS class identifier QoS—Quality of service RRC—Radio resource controller RTD—Round trip delay Rx—Receiver SGW—Serving gateway SINR—Signal-to-noise ratio Tx—Transceiver UE—User equipment UL—Uplink

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 oriented connections based on the type of UEs that are moving into an eNodeB. Thus, these models can act as

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A13/A16 HRPD BTS 3GPP—3rd Generation Partnership Project 3GPP2—3rd Generation Partnership Project 2 AAA—Authorization, authentication, and accounting AN—Access network BTS—Base transceiver station eAN—Evolved access network eHRPD—Evolved HRPD eNodeB—Enhanced NodeB

EPS—Evolved Packet System E-UTRAN—Evolved UTRAN HRPD—High rate packet data HSGW—HRPD serving gateway HSS—Home subscriber server IMS—IP Multimedia Subsystem IP—Internet Protocol ISDN—Integrated services digital network MME—Mobile management entity

PCF—Packet control function PCRF—Policy charging rules function PDN—Packet data network PSS—PSTN/ISDN simulation subsystem PSTN—Public switched telephone network UMTS—Universal Mobile Telecommunications System UTRAN—UMTS Terrestrial Radio Access Network

Figure 3. Monitoring using Celnet Xplorer for the non-roaming architecture of 3GPP ⴙ eHRPD access.

0.01% and 99.99% point-wise predictive confidence interval The one-step-ahead forecast (prediction)

Raw data in square root scale 12 Count 8 (square root 4 scale) 0 Jul 23

Jul 25

Jul 27

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EV-DO—Evolution data optimized

Figure 4. Monitoring the number of attempted connections for a base station in an EV-DO network where the data is observed every five minutes over a two week period.

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OAM SON engine

SON control actions

CX-LTE CX-SON: KPI extraction prediction models DB

LTE network

Data analysis/ report Data collection/ pre-analysis

Data packets

CX—Cellnet Xplorer DB—Database KPI—Key performance indicator LTE—Long Term Evolution OAM—Operations, administration, and maintenance SON—Self-organizing network

Figure 5. Extended functional architecture of Celnet Xplorer for SON.

a trigger for SON to initiate proactive optimization steps.

SON Use Cases In 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 reconfiguration 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 Optimization From 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 connection with the eNB. In LTE_ACTIVE state, the network 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 packets. In LTE_IDLE state, the location of the UE is known at the granularity of a tracking area that consists of multiple eNBs. To track user equipment, the mobility management entity records the TA in which each user is registered. 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 overhead. When there is a UE-terminated call, MME broadcasts 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 successfully 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 planning 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,

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Monitor SON capture & detection UE application’s VoIP/video packets on BE bearer SON analysis & trigger 1. Upgrade VoIP/video flow request & application flow details 2. PCRF initiated IP-CAN session modification 3. Create dedicated bearer request 4. Create dedicated bearer request 5. E-RAB setup request Activate dedicated EPS bearer context request 6. RRC connection reconfiguration 7. RRC connection reconfiguration complete 8. E-RAB setup response 9. Direct transfer (activate dedicated EPS bearer context accept) 10. Uplink NAS (activate dedicated EPS bearer context accept) 11. Create dedicated bearer response 12. Create dedicated bearer response 13. PCRF initiated IP-CAN session modification end 14. Upgrade response

BE—Best effort EPS—Evolved Packet System E-RAB—E-UTRAN radio access bearer GW—Gateway IP—Internet Protocol

IP-CAN—IP Continental Area Network MME—Mobility management entity NAS—Non-access stratum PCRF—Policy charging rules function PDN—Packet data network

RRC—Radio resource controller SON—Self-organizing network UE—User equipment VoIP—Voice over IP

Figure 6. Application-based bearer assignment.

•

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 statistics is required for stable optimization. Incorporating time of day and day of week patterns would further strengthen the algorithm. Application-Based Automatic Bearer Assignment Figure 6 provides an illustration of applicationbased bearer assignment. In this use case, we consider a smartphone or a personal computer (PC) card that initiates a video or VoIP call. The UE’s packets are carried

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over the S1-U interface from the eNodeB to the SGW. The SON capture module does a deep packet inspection 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

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 GBRVoIP 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 corresponding 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 configuration 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 parameters, 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 acknowledges 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 PCRFinitiated IP-connectivity access network (CAN) session modification procedure after the completion of IP-CAN bearer signalling. This completes the automatic detection and provisioning of appropriate bearers for the VoIP/video calls from a third party application running on top of a smartphone or PC card. Network-Wide Optimization Wireless networks as a whole are complex and multiply coupled structures. Sometimes a local change in one cell can cause problems in a previously untroubled 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 developed 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

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Figure 7. Celnet Xplorer-generated traffic density map of lost calls for a 3G1X network gather for a 12 hour period aggregated into 250 meter bins. Monitored cells are circled. Darker shades of gray/black indicate a higher number of lost calls for that grid.

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 locations 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 accurate optimizations. The optimization can be run for small cell clusters and/or scaled up to the entire network. The important aspect of this type of optimization is that the SON engine looks over a larger area of the network so if the optimization is on a smaller

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cluster of cells, the tool understands and models how the changes to this small cluster impact the entire network. This optimization, as stated before, could be used to retune the network for cell additions or traffic 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 network 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 performed more accurately.

(a) Measured mobile density from 7:20 to 7:30 pm

(b) Measured mobile density from 7:30 to 7:40 pm

3G—Third generation

Figure 8. Traffic density variations as measured by Celnet Xplorer for a 3G1X network. Substantial traffic density variations appear even on intermediate timescales in this four cell cluster.

Conclusion Our 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 performance data regarding network and services operation and end user experience. We provide a framework for utilization of this analysis by additional 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 minimizes 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 networks 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. Selfoptimization 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 accommodate the technology demands of running the tens of thousands of different mobile applications that are viewed as the next tech industry wave [10]. Acknowledgements We acknowledge Kenneth Del Signore for providing valuable insights into some of the issues related to paging load optimization. We also thank Aiyou Chen and Jin Cao for their contribution towards statistical models implemented in the EV-DO version of Celnet Xplorer and their suggestions for LTE implementation. *Trademarks Android is a trademark of Google, Inc. Apple and iPhone are registered trademarks of Apple Computer, Inc.

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References [1] 3rd Generation Partnership Project, “SelfConfiguring and Self-Optimizing Network Use Cases and Solutions (Release 8),” 3GPP TS 36.902, v1.0.1, Sept. 2008, ⬍http://www.3gpp.org/ftp/Specs/ html-info/36902.htm⬎. [2] 3rd Generation Partnership Project, “SelfOrganizing Networks (SON), Concepts and Requirements (Release 8),” 3GPP TS 32.500, v8.0.0, Dec. 2008, ⬍http://www.3gpp.org/ ftp/Specs/html-info/32500.htm⬎. [3] 3rd Generation Partnership Project, “SelfOptimization OAM, Concepts and Requirements (Release 9),” 3GPP TS 32.521, v1.1.0, July 2009, ⬍http://www.3gpp.org/ ftp/Specs/html-info/32521.htm⬎. [4] 3rd Generation Partnership Project, “Evolved Universal Terrestrial Radio Access (E-UTRA) and Evolved Universal Terrestrial Radio Access Network (E-UTRAN), Overall Description, Stage 2 (Release 9),” 3GPP TS 36.300, v9.1.0, Sept. 2009, Section 22, ⬍http://www.3gpp.org/ftp/Specs/ html-info/36300.htm⬎. [5] S. C. Borst, A. Buvaneswari, L. M. Drabeck, M. J. Flanagan, J. M. Graybeal, G. K. Hampel, M. Haner, W. M. MacDonald, P. A. Polakos, G. Rittenhouse, I. Saniee, A. Weiss, and P. A. Whiting, “Dynamic Optimization in Future Cellular Networks,” Bell Labs Tech. J., 10:2 (2005), 99–119. [6] A. Buvaneswari, B. Ravishankar, J. M. Graybeal, M. Haner, and G. Rittenhouse, “New Optimization and Management Services for 3G Wireless Networks Using CELNET Xplorer,” Bell Labs Tech. J., 9:4 (2005), 101–115. [7] J. Cao, A. Chen, T. Bu, and A. Buvaneswari, “Monitoring Time-Varying Network Streams Using State-Space Models,” Proc. 28th IEEE Internat. Conf. on Comput. Commun. (INFOCOM ‘09) (Rio de Janiero, Brazil., 2009), pp. 2721–2725. [8] L. M. Drabeck, M. J. Flanagan, J. Srinivasan, W. M. MacDonald, G. Hampel, and A. Diaz, “Network Optimization Trials of a VendorIndependent Methodology Using the Ocelot® Tool,” Bell Labs Tech. J., 9:4 (2005), 49–66. [9] Open Handset Alliance, “Android Open Source Project,” ⬍http://www. openhandsetalliance.com/android_overview.html⬎. [10] T. Woody, “IPhone Apps: A Launch Point for a New Wave of Tech Giants?” Los Angeles Times,

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Business Section, Dec. 22, 2009, ⬍http://articles.latimes.com/2009/dec/22/busi ness/la-fi-iphones-venture22-2009dec22⬎. (Manuscript approved May 2010) ARUMUGAM BUVANESWARI is a research engineer in the End-to-End Wireless Networking Department at Alcatel-Lucent Bell Labs in Murray Hill, New Jersey. She holds a B.E. degree in electronics and communication from Thiagarajar College of Engineering, Madurai, India, and a master of science degree in electrical communication engineering from the Indian Institute of Science, Bangalore, India. During her tenure at Bell Labs, she has focused on data analysis and optimization of 3G1X, EV-DO, and LTE networks. She is the co-inventor of the Celnet Xplorer tool, and she played a lead role in its productization. Her research interests are in optimization of radio access and core networks through real network data, 4G wireless systems, and embedded systems. She has a number of publications in the areas of root cause analysis of radio network failures, dynamic optimization of radio networks, statistical representation of wireless calls, and digital signal processing algorithms and firmware. LAWRENCE DRABECK is a research engineer at Alcatel-Lucent Bell Labs in Holmdel, New Jersey. He joined Bell Labs after completing his Ph.D. in physics at the University of California Los Angeles. His initial work was focused on radio frequency (RF) properties and potential wireless applications of hightemperature superconductors. He has also worked on next-generation radio front ends, interference modeling, and smart antennas. He is now part of the Bell Labs E2E Wireless Networking Group, where he works on real time network monitoring and optimization. NACHI NITHI is a member of technical staff in the Mathematics of Networks and Communications Research Department at Alcatel-Lucent Bell Labs in Murray Hill, New Jersey. He earned a B.E. (honors) in electrical engineering from Madras University, Chennai, India; an M.E. in computer science from Anna University, Chennai, India; and a Ph.D. in computer science from Colorado State University, Fort Collins. He is a member of IEEE. He has published

papers in leading journals and conferences and holds several patents. His main interests are in tools for optical network design, switching center design, and 3G and 4G wireless network monitoring; system simulations; and self-optimization network applications. MARK HANER is a research manager in the Networking and Network Management Research Domain at Alcatel-Lucent Bell Labs in Murray Hill, New Jersey. He holds B.S., M.S., and Ph.D. degrees in electrical engineering and physics from the University of California at Berkeley, where he also held a Miller Research Institute fellowship. Dr. Haner has focused his research activities on broadband access and fixed and mobile wireless systems. His current interest is in network and application performance in 3G and 4G mobile networks such as LTE. He has served on advisory committees for both DARPA and NSF.

from Rutgers University in New Jersey. While in Lucent Technologies’ Mobility Division, one of the many activities in which she was involved was the system modeling of the UMTS baseband processor, OneChip. Currently, she is working on the Celnet Xplorer, a high speed network performance monitoring tool. Her research interests include mobile core network evolution to efficiently manage the traffic explosion and managing network resources to deliver services to the end user at an exceptional level of quality of service. ◆

PAUL POLAKOS is a director in the Networking and Network Management Research Domain at Alcatel-Lucent Bell Labs. He is currently based in Nozay, France. His focus at Bell Labs is physics and wireless research. He has been instrumental in the definition and development of key technology initiatives for digital wireless systems, including intelligent antennas (IA) and the multiple input multiple output (MIMO) Bell Labs Layered Space-Time (BLAST) advanced base station and radio access network architectures; radio signal processing; enhancements to wireless networks for high data rates and high capacity; and dynamic network optimization. He holds B.S., M.S., and Ph.D. degrees in physics from Rensselear Polytechnic Institute in Troy, New York, and the University of Arizona in Tucson. Prior to joining Alcatel-Lucent, he was actively involved in elementary particle physics research at the U.S. Department of Energy’s Fermilab and at the European Organization for Nuclear Research (CERN) and was on the staff of the Max Planck Institute for Physics and Astrophysics in Munich. He is author or coauthor of more than 50 publications and holds numerous patents. CHITRA SAWKAR is a member of technical staff in the E2E Wireless Networks Research Department at Alcatel-Lucent Bell Labs in Murray Hill, New Jersey. She received her bachelor’s degree in electrical engineering from the University of Madras in Chennai, India, and M.S. in electrical and computer engineering

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