Self-Configuration and Self-Optimization for LTE Networks 2010

INTRODUCTION

Future mobile communication systems will con-struct a flexible service environment, includingnetwork operator, vendor, access provider, ser-vice provider, software developer, contentprovider, and customer. It can be foreseen thatthe business models in Long Term Evolution(LTE) systems will be cross-linked abundantlyand provide a wider range of market. On onehand, with the usage of advanced access andtransmission techniques, both the transmissionbandwidth and quality of service (QoS) ofmobile networks have been greatly improved.New wireless service models and applicationshave been developed with increasing spectrumband and data rate. On the other hand, futuremobile networks are also required to be costeffective and easy to deploy. The expectationsof customers on coverage and QoS increasecontinuously. Thus, the requirements on futureLTE networks will be high from the early stageof their deployment. More efficient strategiesand algorithms have to be integrated in the

future mobile networks to further reduce capitalexpenditures (CAPEX) and operational expen-ditures (OPEX).

Network optimization is one of the key partsin the life cycle of mobile systems. For second-generation (2G) mobile networks, a series ofstandardized procedures have been defined forwireless network planning and optimization,while for third-generation (3G) mobile net-works, researchers, and engineers are testingand improving the network planning and opti-mization methods/tools. For example, ChinaMobile is improving their optimization suitefor time-division synchronous code-divisionmultiple access (TD-SCDMA) networks duringthe ongoing commercial deployment. For both2G and 3G mobile systems, network optimiza-tion should involve base station maintenance,signaling, testing, adjustment, data collection,and analysis functions to improve coverage andreduce interference. However, the deploymentand optimization of mobile networks are verycomplicated and challenging engineering tasksthat require a comprehensive systematicapproach; conventional procedures usually costa long time, and a lot of resources and man-power to achieve the goal. In future mobilenetworks, wherein multiple types of cells (e.g.,macro, micro, pico, and even femto) will coex-ist, an increasing number of parameters needto be taken into account in network optimiza-tion, so the challenges become much moreintensive.

The revenue from a mobile network highlydepends on its operational efficiency; hence,operators are seeking advanced technologiesand proper strategies to reduce the OPEX ofLTE networks. This motivates the research anddevelopment of more efficient network archi-tecture and new technologies, such as mobilityload balancing (MLB), for LTE networks [1, 2].Driven by both the market perspective and thetechnological potentials, self-organizing net-work (SON) technology is promoted by theinternational standardization body Third Gen-eration Partnership Project (3GPP) [3] andoperators’ lobby Next Generation Mobile Net-

IEEE Communications Magazine • February 201094 0163-6804/10/$25.00 © 2010 IEEE

ABSTRACT

With the rapid growth of mobile communica-tions, deployment and maintenance of cellularmobile networks are becoming more and morecomplex, time consuming, and expensive. Inorder to meet the requirements of network oper-ators and service providers, the telecommunica-tion industry and international standardizationbodies have recently paid intensive attention tothe research and development of self-organizingnetworks. In this article we first introduce boththe market and technological perspectives forSONs. Then we focus on the self-configurationprocedure and illustrate a self-booting mecha-nism for a newly added evolved NodeB withouta dedicated backhaul interface. Finally, mobilityload balancing as one of the most important self-optimization issues for Long Term Evolutionnetworks is discussed, and a distributed MLBalgorithm with low handover cost is proposedand evaluated.

LTE TECHNOLOGY UPDATE

Honglin Hu and Jian Zhang, Chinese Academy of Sciences

Xiaoying Zheng, Chinese Academy of Sciences and Southeast University

Yang Yang, Chinese Academy of Sciences and University College London

Ping Wu, Uppsala University

Self-Configuration and Self-Optimization for LTE Networks

HU LAYOUT 1/20/10 11:15 AM Page 94

IEEE Communications Magazine • February 2010 95

works (NGMN) [4]. For example, 3GPP has setup an ad hoc SON group in System Architec-ture Working Group 5 (SA5) and has standard-ized some SON-related techniques for LTEnetworks in Radio Access Network WorkingGroup 3 (RAN3) and RAN2. Worldwide, manyresearch projects have been funded to developkey SON technologies, such as the SOCRATESproject in Europe [5].

The self-organization capability of a mobilenetwork mainly includes three aspects: self-con-figuration, self-optimization, and self-healing.Figure 1 gives a comparison between networkoperation with SON functions and the conven-tional mechanism, which relies on human inter-vention or service tools. The self-configurationcapability enables fast installation and deploy-ment of future evolved NodeBs (eNBs), whichreduces human involvement and deploymenttime. Moreover, the newly added eNBs can beintegrated in a plug-and-play approach. Thus,self-configuration is especially useful at the pre-operational stage of a wireless mobile network.Comparably, self-optimization techniques suchas MLB enable a mobile network to automati-cally select and adjust proper algorithms and sys-tem parameters, to achieve optimal systemcapacity and service coverage. Therefore, self-optimization techniques are crucial for the oper-ational state of mobile networks. Finally,self-healing assists operators in recovering a net-work when it collapses due to some unexpectedreason. It can be seen as an event-driven pro-cess, which is necessary at emergent system fail-ures.

In this article we mainly consider the self-configuration and self-optimization of mobilenetworks. Specifically, we first illustrate a self-configuration mechanism for newly added eNBswithout dedicated backhaul interfaces in thenext section. Then we propose and evaluate adistributed MLB algorithm with low handovercost for LTE networks. Finally, we conclude thisarticle in the final section.

SELF-CONFIGURATION MECHANISMS

OVERVIEW

In the LTE overall description specification [6],the self-configuration process is defined as theprocess where the newly deployed eNBs are con-figured by automatic installation procedures toget basic parameters and download necessarysoftware for operation. On the other side, self-configuration will also be applied in failure casesin combination with fast failure detection mech-anisms to provide automatic failure recovery orcompensation mechanisms (e.g., in cell outagecases).

The self-configuration process takes place atthe pre-operational state. First, an eNB gets theIP addresses of itself and the operation, adminis-tration, and maintenance (OAM) center. Thenthe eNB associates with an access gateway(aGW) after it is authenticated to the network.After that, the eNB downloads the required soft-ware and the operational parameters. Finally,the eNB configures the neighbor list and thecoverage/capacity related parameters accordingto the downloaded information, and then entersthe operational state.

In conventional cellular systems an eNB (orbase station) has at least two interfaces, the airinterface to user equipment (UE), and the back-haul interface to the core network. Obviously,the self-configuration process uses the backhaulinterface. The main point of implementing theself-configuration function is how the eNB getsits IP address and connects to the configurationserver. There are several solutions to this issue.Simply, the eNB can use Dynamic Host Configu-ration Protocol (DHCP) or Bootstrap Protocol(BOOTP) agent to get its IP address. The DHCPor BOOTP broadcast packets can only be trans-mitted in the same subnet.

If the routers in the backhaul link do not sup-port DHCP or BOOTP , other schemes should beused. For example, as in [7], an eNB is added intoa multicast group of routers and a configuration

Figure 1. Network operation a) without; b) with SON functions.

Configuration

eNB power on

Human/tools Pre-operationalstate

Operationalstate

eNB power on

Self-configuration

SONcoordinatorOptimization Human/tools

Healing Human/tools

(a) (b)

Self-optimization

Self-healing


IEEE Communications Magazine • February 201096

server using the Internet Group ManagementProtocol (IGMP), and the configuration informa-tion is received in the multicast IP packet.

CHALLENGES AND SOLUTIONSThe self-configuration methods introducedabove all assume that the eNB has a dedicatedbackhaul interface, and the eNB uses this inter-face connecting to the configuration server. Cur-rently, the physical backhaul interface can be atime-division multiplexing (TDM) or Ethernetinterface. However, in the following cases theeNBs may not communicate through the dedi-cated backhaul interface:• When an eNB has a physical backhaul inter-

face, but it is deployed for temporal cover-age, there may be no matching interfaces atthe place where the eNB is located.

• An eNB may not have a dedicated backhaulinterface. Along with the revolution inbroadband mobile networks, both the datarate achieved by the cellular system and thedensity of eNBs are increasing. Thus, thecost of the backhaul network becomes amain burden of operators, and the require-ment of reducing the backhaul cost is raisedin both 3GPP and NGMN. To reduce thebackhaul cost, backhaul transmission proto-cols of eNBs are proposed to be built onthe radio access technology [8].Moreover, as the operational frequency might

be higher in the next-generation cellular system,the density of eNBs will be higher as well. Thus,the probability of an eNB without a dedicatedbackhaul interface will also increase. In such

cases the traditional self-configuration solutionscannot work, and a more flexible mechanism isneeded. To start the self-configuration process inthese cases, we propose that a newly deployedeNB shall boot up in UE mode first, and thenconnect to a neighbor eNB in operational state(i.e., already configured) to download the config-uration parameters and eNB software. Then thenewly added eNB can easily be configured andsmoothly enter the operational state.

Figure 2 shows the proposed neighbor assist-ed self-configuration procedure. The detailedsteps are described as follows:1 When a new eNB is powered on, it starts

up in UE mode. The UE mode does notneed to be configured.

2 The new eNB scans the neighbor cells.3 The new eNB chooses one of the neighbor

eNBs that already has a backhaul link as itssponsor eNB. The backhaul link can beeither wired or hybrid.

4 The new eNB gets access to the sponsoreNB by a random access procedure. To dif-ferentiate the new eNB from normal UEand reduce the access time, some dedicatedrandom access channels/codes can be usedfor the new eNB.

5 The new eNB sends the authenticationinformation to the sponsor eNB. If the neweNB uses the same random access proce-dure as the UE, a flag should be sent alongwith the authentication message to indicatethat it is different from UE. The sponsoreNB forwards the authentication request tothe OAM if it finds that the authentication

Figure 2. Neighbor eNB assisted self-configuration procedure.

10. Operational state

1. Startup in UE mode

New eNB Sponsor eNB Wireless

3. Choose a neighbor

8. Start eNB mode

7. Configuration

2. Scan neighbor eNBs

4. Random access

Configuration server/OAM Wired or hybrid

Backhaul is established

5. Authentication and IP address allocation

6. Download software and configure parameters

9. Establish backhaul links

The self-configura-

tion methods

introduced above all

assume that the eNB

has a dedicated

backhaul interface,

and the eNB uses

this interface

connecting to the

configuration server.

Currently, the

physical backhaul

interface can be a

TDM interface or an

Ethernet interface.



is from a new eNB. After the new eNB isauthorized, OAM sends the IP addresses ofthe new eNB, the aGW, and the configura-tion server to the sponsor eNB. Finally, thesponsor eNB forwards the IP addresses tothe new eNB.

6 The new eNB connects to the configurationserver, and then downloads the softwareand operational parameters. This step canbe replaced by other alternatives. For exam-ple, the new eNB can also use lower-layersignaling to request the sponsor eNB todownload the eNB software and opera-tional parameters, and then the sponsoreNB sends these data to the new eNB.

7 After having received the eNB software andoperational parameters, the new eNBinstalls the software and configures itself.

8 The new eNB terminates the UE mode andswitches to the eNB mode.

9 The new eNB establishes backhaul connec-tion with both the neighbor eNBs and thecore network.

10The self-configuration procedure com-pletes, and the new eNB enters the opera-tional state.The above proposed procedure is a more

general solution to the eNB self-configurationissue, since this mechanism is independent of thecore network type. As long as a neighbor eNB isconfigured and connected to the core network,the newly deployed eNB can easily be self-con-figured. Obviously, the proposed approach canalso be used for a newly added relay station ifneeded.

MOBILITY LOAD BALANCING WITHLOW HANDOVER COST

OVERVIEWIn cellular networks the arrivals of mobile usersand the resulting traffic load are random, time-varying, and often unbalanced, which make cellloads in a system unequal. There might be ahuge amount of UE in some cells, which areoverloaded; while, there is much less UE inother cells where resources are not fully utilized.The inefficient resource utilization may be miti-gated by optimal network management and plan-ning. However, the current network planningstrategies are far from solving the load balancingproblem in LTE systems completely.

There are several facts that make the MLBproblem a critical challenge faced by LTE net-works. First, network applications and servicesdevelop rapidly, and the demand for resourcesincreases very fast, which make resource short-age in cellular networks very common. Second,the traffic is time-varying and unpredictable.Therefore, static and pre-fixed network planningcannot make the network adapt to the varyingload dynamically in a timely fashion. Third,remaining competitive at a reduced cost by uti-lizing resources efficiently is the driver of study-ing the MLB problem from the market side.

One possible way to balance load is to shiftsome UE at the border of overlapping or adja-cent cells from more congested cells to less con-gested cells, which is often referred to as

handover or handoff. By changing the assignedeNBs for UE, the load is balanced and systemperformance is improved at the cost of systemoverhead caused by handovers. The handoverprocedure consumes substantial systemresources, and the involved UE may experiencesignificant system delay and performance degra-dation. Hence, handovers cannot happen arbi-trarily often; otherwise, the performanceimprovement gained by the more balanced traf-fic load cannot compensate for the performanceloss caused by the handovers.

In the following, we aim to balance theunequal traffic load, improve the system perfor-mance, and minimize the number of handoversneeded to achieve load balancing. A new MLBalgorithm is proposed, which has performancebounds on both the average system delay andthe average number of handovers that havetaken place. In contrast, the primary interest oftraditional load balancing studies is to optimizesystem performance in terms of throughput, fair-ness, and system delay. Hence, they do notintend to restrict the occurrence of handoversand might result in a solution with too frequenthandovers.

MLB ALGORITHM WITHPENALIZED HANDOVERS

Consider a downlink constrained packet-switched multicell network. Let U denote theset of UE. Each UE unit, u ∈ U, is assigned toa cell as its serving cell . Let us denoteDataRate(u, c) the achieved data transmissionrate if serving cell c transmits data to UE u.The transmission rates from cells to UE aredetermined by instant channel conditions,power allocation, and the resulting interfer-ence. The channel conditions, power allocation,and interference level can be sensed and knownby UE and eNBs. Hence we assume the instanttransmission rates are determined by eNBs andare known. For each UE unit u, we assume itsdata packets arrive at the backbone networkrandomly and are buffered at the serving eNB.At each eNB, the queues are UE differentiated.Let us denote the backlog of the queue for u byQueueBacklog(u).

It is reasonable to assign u to a cell provid-ing the largest QueueBacklog(u) × DataRate(u,c), which helps to maintain system stability andachieve the largest capacity region. In contrast,since the procedure of handovers is costly andcannot happen arbitrarily often, it might bebeneficial to keep UE associated with the cur-rent serving cell c even if a neighbor cell c′ canprovide a bit larger QueueBacklog(u) ×DataRate(u, c′). By considering the two con-flicting motivations together, we propose toassign a penalty to a pair of UE u and cell c ifa handover is required to make u assigned to c.Let PenaltyFactor(u, c) ∈ [0, 1] be a penaltyfactor if a handover toward cell c is requiredfor UE u. Now we propose to assign u to thecell with the largest QueueBacklog(u) ×DataRate(u , c) × (1 – PenaltyFactor(u , c)),which is denoted W(u, c).

Furthermore, in order to reduce the systemoverhead caused by handovers, for each cell we

The primary interest

of traditional load

balancing studies is

to optimize system

performance in

terms of throughput,

fairness, and system

delay. Hence they do

not intend to restrict

the occurrence of

handovers and might

result in a solution

with too frequent

handovers.



only allow at most one UE unit to hand overtoward the cell at a time; that is, the UE canhand over toward at most one cell, and a cellcan accept at most one UE from other cells. Atthe same time, we wish to maximize ∑u∈U W(u,c). Hence, the problem of selecting a subset ofUE for handovers reduces to a MaximumWeighted Matching (MWM) problem, whereUE u is matched to cell c and W(u, c) is theassociated weight. We use the greedy distributedalgorithm with low complexity and system over-head proposed in [9] to solve the MWM prob-lem in our proposed MLB algorithm.

The proposed MLB procedure stabilizes thesystem and makes a trade-off between the aver-age queue backlog and the number of handovers.For a larger penalty factor, the algorithm choos-es to reduce the number of handovers at the costof the larger average queue backlog (i.e., thelarger average system delay); on the other hand,with the smaller penalty factor, it prefers to havethe shorter system delay with an increased num-ber of handovers. The detailed steps of the algo-rithm are illustrated as follows:

1 Each UE u computes the weight W(u, c) foreach cell c with which it is able to commu-nicate.

2 The greedy algorithm [9] is run to solve theMWM problem approximately based on theweights computed in step 1.

3 If UE u is assigned to cell c, which is notthe current serving cell of u, UE u handsover toward cell c.The proposed algorithm can be run repeated-

ly at different frequency, varying from severaltime slots to several minutes, which balances var-ious aspects of the algorithm (e.g., performanceand system overhead). In the simulation we didexperiments with the algorithm running at differ-ent frequencies.

NUMERICAL EXAMPLESWe did experiments on a two-tier multicell net-work, and the detailed simulation setup isdescribed in Table 1. Each UE is initially allo-cated to its eNB based on signal-to-interfer-ence-plus-noise ratio (SINR). The UE units areassumed to be static during the simulation.When generating the channel gains, the pathloss model in Table 1 and the log-normal shad-owing are considered. We use channel condi-tions to model other factors that can impact thelink transmission rate, such as environmentaleffects or wireless fading. We assume thatunder a better channel condition, a more effi-cient physical layer modulation and codingstrategy is used to support a higher data ratefor the channel. The condition of each channelis generated randomly and independently ateach time slot.

Performance Evaluation Metrics — For intra-cell scheduling there are two well-known policiesadopted in practice, SINR-based and Propor-tional Fair (PF). The SINR based policy aims tomaximize the system throughput, while the PFpolicy maintains proportional fairness amongUEs [10]. Both policies are used in our simula-tion.

We have run four tests. In tests 1 and 2, han-dovers are absolutely not allowed. The SINR-based and PF intra-cell scheduling policies areadopted, respectively. The system throughput oftest 1 and the worst case queue backlog of test 2serve as the benchmarks of system performance.In both tests 3 and 4, the PF policy is used forintra-cell scheduling. In test 3, in each time slotwe run the proposed algorithm once to balancethe traffic load. Meanwhile, we only run thealgorithm once every 1000 time slots in test 4.Since the load balancing procedure runs at ahigher frequency in test 3, it causes higher sys-tem overhead. In both tests 3 and 4, the penaltyfactors used are 0.1 for the reassignment of UEinvolving handovers.

We summarize all the tests in the following.• Test 1: SINR-based intra-cell scheduling.

No handovers are allowed.• Test 2: PF intra-cell scheduling. No hand-

overs are allowed.• Test 3: PF intra-cell scheduling. The algo-

rithm runs in each time slot.• Test 4: PF intra-cell scheduling. The algo-

rithm runs every 1000 time slots.Table 1. Simulation setup.

Network topology

Number of cells 19

Number of sectors per cell 3

Distance between origins 1.732 km

Channel model

Frequency reuse factor 3

Path loss model pl(d) = –35.63 – 35 log10(d)

Log-normal shadowing Standard deviation = 8.9 dB

Channel conditions Good, median, poor

Probability in each condition 1/3

System bandwidth 10 MHz

UE distribution

Number of UE units 1197

Distribution in each sector Uniform distribution

No. of sectors (9 UEunits/sector) 19

No. of sectors (18 UE units/sector) 19

No. of ectors (36 UE units/sector) 19

Miscellaneous

Data arrival Poisson process

Time slot length 1 ms

Simulation time 40,000 time slots



Results Analysis — In Fig. 3 we show theresults of tests 1 to 4 in terms of system through-put and the worst case queue backlog of UE.The throughput is normalized with respect totest 1, and the worst case queue backlog is nor-malized with respect to test 2. Regarding themaximum system throughput, tests 3 and 4 (withload balancing) improve system throughput by12 to 13 percent, which will be even larger ifcompared to test 2. Consider the worst casequeue backlog, which is proportionally related tothe worst case system delay UE experiences. Theworst case backlogs achieved by tests 3 and 4 aresmaller than what test 2 obtains, which meanstests 3 and 4 maintain good fairness. The num-ber of handovers that have taken place in tests 3and 4 are 904 and 516, respectively. In test 4 theproposed algorithm runs at a much lower fre-quency and requires about 40 percent fewerhandovers than test 3. Meanwhile, we see thatthe system performance of tests 3 and 4 is com-parable from Fig. 3. Hence, the proposed algo-rithm can run at a relatively low frequency andstill achieve good performance. We summarizethat the proposed MLB algorithm achieves a sig-nificant improvement at the cost of affordablecomplexity and system overhead.

Next we test the proposed MLB algorithmwith different penalty factors. The larger thepenalty factor, the more penalties a handoverwill receive. In Fig. 4a the worst case queuebacklogs are normalized with respect to the testwhere the penalty factor is 0.05. Note that hand-overs are more heavily penalized when the penal-ty factor becomes larger. Intuitively, as thepenalty factor becomes larger, fewer handoverstake place and the worst case queue backlogbecomes larger. Figure 4 shows that the numberof handovers drops while the worst case queuebacklog increases as the penalty factor increases,which verifies the intuition and can be justifiedas the trade-off between the number of hand-overs and the worst case queue backlog. Withthe largest penalty factor we tested (i.e., 0.8), theworst case queue backlog increases by 15 per-cent, compared to the test with the penalty fac-

tor 0.05. Meanwhile, the number of handoversthat have taken place drops dramatically from1600 to about 200. Hence, by selecting differentpenalty factors, the algorithm prefers differentsystem performance metrics, either the experi-enced system delay or the handover overhead.

CONCLUSIONSSON technologies possess a clear and predomi-nant perspective for LTE mobile networks, andthus have attracted much attention from bothindustry and academia. Meanwhile, many chal-lenges still remain in the R&D of feasible self-configuration and self-optimization techniquesfor LTE networks, which require more efficientalgorithms and network architectures. As thedensity of eNBs increases in future mobile net-works, the proposed self-configuration mecha-nism can be widely used for the newly added

Figure 3. Comparison of normalized throughput and worst-case queue backlog.

Test index

10

1

0.9

Nor

mal

ized

val

ue

1.1

1.2

1.3

2 3 4

ThroughputQueue backlog

Figure 4. Comparisons of different penalty factors: a) normalized worst-case queue backlog; b) number of handovers that took place.

Penalty factor

(a) (b)

0.20

0.95

0.9

Nor

mal

ized

que

ue b

ackl

og

1

1.05

1.1

1.15

1.2

0.4 0.6 0.8 1

Penalty factor

0.20

400

200

Num

ber

of h

ando

vers

600

800

1000

1200

1400

1600

0.4 0.6 0.8 1



eNBs without dedicated backhaul interfaces.Moreover, numerical results show that by takinginto account handover penalty, our proposedMLB self-optimization algorithm can significant-ly improve system performance.

ACKNOWLEDGMENTSThis work is partially supported by the Sino-Swedish Cooperative Program 2008DFA12090from MOST of China, the National Natural Sci-ence Foundation of China (NSFC) under grant60902041, and the open research fund of theNational Mobile Communications ResearchLaboratory, Southeast University, China.

REFERENCES[1] NEC, “Self Organizing Network: NEC’s Proposals for

Next-Generation Radio Network Management,” Feb.2009; http://www.nec.com

[2] M. Dottling and I. Viering, “Challenges in Mobile Net-work Operation: Towards Self-Optimizing Networks,”Proc. IEEE Int’l. Conf. Acoustics, Speech, and Sig. Pro-cessing, Apr. 2009, pp. 3609–12.

[3] 3GPP Tech. Rep. TR 36.902 v. 1.2.0, “Self-Configuringand Self-Optimizing Network Use Cases and Solutions,”May 2009.

[4] NGMN, “Use Cases Related to Self-Organising Network,Overall Description,” v. 2.02, Apr. 2007; http://www.ngmn.org

[5] INFSO-ICT216284 SOCRATES, “Framework for the Devel-opment of Self-Organisation Methods,” DeliverableD2.4, v. 1.0.3, July 2008; http://www.fp7-socrates.org/files/Publications/

[6] 3GPP TS 36.300 v. 8.8.0, “Evolved Universal TerrestrialRadio Access (E-UTRA) and Evolved Universal TerrestrialRadio Access Network (E-UTRAN), Overall Description,Stage 2, Release 8,” 2009.

[7] Alcatel-Lucent, “Automated Installation of eNodeBs,”R3-071251, 3GPP TSG RAN3 #56bis, Sophia Antipolis,2007.

[8] Ericsson, “Self Backhauling and Lower Layer Relaying,”R1-082407, 3GPP TSG-RAN WG1 #53bis, Warsaw,Poland, 2008.

[9] X. Lin, N. B. Shroff, and R. Srikant, “The Impact ofImperfect Scheduling on Cross-Layer Rate Control inWireless Networks,” IEEE/ACM Trans. Net., vol. 14, no.2, Apr. 2006, pp. 302–15.

[10] T. Bu, L. Li, and R. Ramjee, “Generalized ProportionalFair Scheduling in Third Generation Wireless Data Net-works,” Proc. IEEE INFOCOM ‘06, Apr. 2006, pp. 1–12.

BIOGRAPHIESHONGLIN HU [M] ([email protected]) received his Ph.D. degree incommunications and information systems in January 2004,from the University of Science and Technology of China(USTC). Then he was with Future Radio, Siemens AG Com-munications, Munich, Germany. In January 2006 he joinedShanghai Research Center for Wireless Communications(WiCO) and is now head of the First R&D department.Meanwhile, he serves as a professor at the Shanghai Insti-tute of Microsystem and Information Technology (SIMIT),

Chinese Academy of Science (CAS). He is Vice Chair of theIEEE Shanghai Section and has served as a TPC member forIEEE ICC ‘06–‘10 and IEEE GLOBECOM ‘07–‘10. He has pub-lished over 50 international journal/conference papers. More-over, he has co-edited four technical books published byTaylor & Francis/CRC Press, of which three have been trans-lated into Chinese as well, and one book on 3GPP LTE inChinese published by the Publishing House of the ElectronicsIndustry (PHEI). Currently, he serves as an Associate Editor oron the Editorial Board of five international journals.

JIAN ZHANG received his B.S. degree in electrical engineeringand information science from USTC, Hefei, in 2004, and hisPh.D. degree from SIMIT, CAS in 2009. He is now withWiCO and also serves as an assistant professor at SIMIT,CAS. His research interests include radio resource manage-ment, multihop wireless communication systems, quality ofservice support over wireless networks, and self-organizingnetworks.

XIAOYING ZHENG received her Bachelor’s and Master’sdegrees in computer science and engineering from Zhe-jiang University, P.R. China, in 2000 and 2003, respectively,and her Ph.D. degree from the University of Florida in2008. She is now with WiCO and also serves as an assis-tant professor at SIMIT, CAS. Her research interests includeapplications of optimization theory in networks, peer-to-peer overlay networks, content distribution, and conges-tion control.

YANG YANG received B.Eng. and M.Eng. degrees in radioengineering from Southeast University, China, in 1996 and1999, respectively, and a Ph.D. degree in information engi-neering from the Chinese University of Hong Kong in 2002.He is currently a senior lecturer with the Department ofElectronic and Electrical Engineering at University CollegeLondon (UCL) and the assistant director if WiCO, CAS. Priorto that he served the Department of Information Engineer-ing at the Chinese University of Hong Kong as an assistantprofessor (August 2002–August 2003), and then theDepartment of Electronic and Computer Engineering atBrunel University, UK, as a Lecturer (September2003–February 2005). He has published over 70 referredjournal and conference papers, covering a wide range ofresearch areas such as next-generation mobile communica-tions, wireless ad hoc, sensor, and mesh networks, cross-layer algorithm design and performance evaluation,cooperative communications, cognitive radio, dynamicradio resource management, and medium access controlprotocols.

PING WU received his B.S. degree in electrical engineeringfrom Nanjing Aeronautical and Astronomical University,China, and M.S. and Ph.D. degrees in electrical and elec-tronic engineering from Xi’an Jiaotong University, China, in1982, 1987, and 1991, respectively. After receiving hisPh.D.degree, he did postdoctoral work at Fraunhofer Institutefor Biomedical Engineering, St. Ingbert, German,y for twoyears (1992–1994). Since October 1994 he has been withUppsala University, Sweden, and is an associate professorin the Division of Signals and Systems of the Departmentof Engineering Sciences at the university. He has authoredand co-authored 16 journal papers and 27 conferencepapers. His current research interests are in wireless meshand sensor networks, OFDM and MIMO, cross-layer designand optimization, network coding, and signal processingfor applications in telecommunications.

As the density of

eNBs increases in

future mobile

networks, the

proposed self-

configuration

mechanism can be

widely used for the

newly added eNBs

without dedicated

backhaul interfaces.


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Self-Configuration and Self-Optimization for LTE Networks 2010