GAME THEORY AND FUZZY BASED LOAD BALANCING TECHNIQUE FOR LTE NETWORKS

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1 GAME THEORY AND FUZZY BASED LOAD BALANCING TECHNIQUE FOR LTE NETWORKS D. HARI BABU 1,Dr. A. SELWIN MICH PRIYADHARSON 2 1 research scholar, school of Electrcal & computng, 1 VEL Tech Unversty, Avad, Chenna, Tamlanadu, Inda 2 Assocate Professor, school of Electrcal & computng, 2 VEL Tech Unversty, Avad, Chenna, Tamlanadu, Inda Emal: 1 babuhar81@gmal.com ABSTRACT In LTE Networks, durng load balancng, the adverse effects of rado lnk falure on the handoff performance are not consdered. In order to overcome ths ssue, n ths paper, we propose to desgn a game theory and fuzzy logc based load balancng technque for LTE networks. In ths technque, the load balancng s trggered based on the status of each cell whch s estmated usng fuzzy logc. Here the metrcs call blockng rato, transmt power, composte avalable and mssng capacty are consdered as nput for the fuzzy logc and the status of cell s determned as output. Based on the cell status, the load balancng s trggered and the dynamc hysteress adjustment s performed based on the game theory model. By smulaton results, we show that the proposed technque mnmzes the rado lnk falure. Keywords: Load Imbalance, Self-Optmzaton, LTE, Fuzzy Logc, Game Theory, 3GPP 1. INTRODUCTION Long Term Evoluton (LTE) standard s made for 4G cellular networks by the 3rd generaton partnershp project (3GPP). LTE ntends at mnmzng the system and User Equpment (UE) complcatons. It allows flexble spectrum deployment n exstng or new frequency spectrum and enables co-exstence wth other 3GPP Rado Access Technologes (RATs). LTE uses sngle-carrer frequency dvson multple access (SC-FDMA) for the uplnk (UL) and orthogonal (OFDMA) n downlnk (DL). Hence t provdes a flexble and spectrally effcent rado lnk protocol desgn wth low overhead meetng the challengng targets to ensure good servce performance n varyng deployments. LTE networks can acheve hgh spectrum effcency due to the usage of mult-nput and mult-output (MIMO) antenna and orthogonal frequency dvson multple (OFDM) technology [1] [3] [5]. 1.1 Objectves of LTE To mnmze the system complexty To mnmze User Equpment (UE) complexty Flexble spectrum deployment [2] [3] [5]. 1.2 Need of Load balancng n LTE The network performance s stll nfluenced by several factors, whch creates nter-cell nterference (ICI) and load mbalance. So there s a requrement for load balancng n LTE. The basc dea of LB s to free from the excessve traffc from hot spots to neghborng low-load cells. The optmzaton targets provde a better utlzaton towards the overall system throughput by provdng better QoS to the end users. A load balancng (LB) scheme s requred to mnmze the demanded rado resources of the maxmum loaded cell to avod the traffc congeston n long term evoluton (LTE) networks. Load mbalance n LTE networks deterorates the system performance nfluenced by unbalanced load dstrbuton among nearby cells. Hence the realtme nter-cell optmzaton adaptable to envronment especally when unbalanced and tme varyng, s needed [3] [5]. 1.3 Issues n Load balancng Handover Consumpton of rado resources Cell-breathng Overlappng area Traffc loads Load dstrbuton [7] [11] [12] 352

2 1.4 Bascs of Game Theory The game Z s defned as Z = (N, S, {UF }). where N = fnte set of players S = acton space formed as Cartesan product..e. S = S 1 S 2 S 3 S 4.. S n UF = utlty functons. UF = {UF 1, UF 2,.., UF n } The outcomes are selected by a partcular player wth S as UF and the partcular actons selected by other players s S -. Ratonalty s the most basc assumpton n game theory. Ratonal players are assumed to maxmze ther payoff, whch s selfsh motvaton. In game theory, outcome s the soluton of a game. In WSN, ntruson detecton system (IDS) acts as one player and ntruder plays as opponent player. In the WSN problem, the large WSN s dvded nto clusters and IDS defends a cluster at any gven tme, whle the attacker dsturbs the normal operatons. The man applcatons of game theory are as follows 1) Decson makng n many economc problems especally durng bddng. 2) Power control to set the power level of nodes. Ths s performed to maxmze ther sgnal nterference to nose rato (SINR), ther selecton of path by source node to mnmze delay, and ther cooperaton among the nodes to dentfy the servce and forwardng of the packets to ther destnaton. 1.5 Problem Identfcaton In load balancng usng Fuzzy Q-learnng optmzaton technque [7], the call blockng rato (CBR) dfference and current handoff (HO) margn are consdered as nput for fuzzy logc and computes the requred HO margn as the output. In [10], the call blockng rato (CBR) dfference and transmttng power (TXP) dfference are consdered as nput for fuzzy logc and computes the requred TXP change as the output. In [11], Composte Avalable Capacty (CAC) and Composte Mssng Capacty (CMC) are consdered for trggerng the load balancng polcy by adjustng the Cell Indvdual Offset (CIO). However the load balancng technques should consder the effects of rado lnk falure on the handoff performance. In [4], a dynamc hysteress adjustment (DHA) s performed based on the handover performance ndcator (HPI) whch ncludes rado lnk falure (RLF) rato. In ths proposal, we propose to desgn a game theory and fuzzy logc based load balancng technque for LTE networks. 2. RELATED WORK LI Bo et al [1] have proposed an nter-doman cooperatve traffc balancng scheme focusng on reducng the effectve resource cost and mtgatng the co-channel nterference n mult-doman Het- Net. In the numercal evaluaton, the genetc algorthm (GA) as an optmzaton method s used to demonstrate that the total effectve resource cost s sgnfcantly reduced through our proposed nterdoman traffc balancng scheme comparng wth the ntra-doman traffc balancng scheme. The 43% of the resource cost s saved. However the cell-edge throughput and the average cell throughput s not ncreased effectvely. Zhhang L et al [2] have proposed an algorthm whch ncludes QoS aware ntra- and nter-cell handover and call admsson control. Ther algorthm can sgnfcantly decrease the new call blockng rate for users wth QoS requrements and mprove the total utlty for users wthout QoS requrements at the cost of a bt degradaton of total throughput. Ahmad Awada et al [3] have presented a gametheoretc analyss for load balancng. Also, they have modeled the utlty functon maxmzed by each player and defned the actons leadng to the Nash equlbrum pont. The load balancng can remarkably ncrease the capacty usage n the network even when the cells act n a noncooperatve way. If the amount of load to accept or to offload s decded ndependently by each cell, we would expect that the attaned Nash equlbrum pont acheves most of the gan ntended from load balancng. Ths ndeed paves the way for the possblty of consderng the deployment of dfferent load balancng algorthms by varous 353

3 manufacturers as the loss n performance would be neglgble. Wenyu LI et al [4] have ntroduced a dynamc hysteress-adjustng algorthm n LTE selforganzaton networks. Furthermore, they take the realstc network stuatons nto account to obtan a more relable result. The proposed method s evaluated by a seres of system-level smulaton whch wtnesses an mprovement n handover performance and number of satsfed users n LTE networks. Omar Altrad et al [6] have proposed a general load-balancng algorthm to help congested cells handle traffc dynamcally. The algorthm can be automatcally controlled and trggered when needed for any cell on the system. It can be mplemented n a dstrbuted or sem-dstrbuted fashon. The trggerng cycle for ths algorthm s left for the operator to decde on; the underlyng varatons are slow so there s no need for fast selfoptmzng network (SON) algorthms. They apply the load-balancng algorthm to an LTE network and dfferent crtera are adopted to evaluate the algorthm's performance. P. Muñoz [7] have proposed the optmzaton of an FLC for load balancng n next generaton wreless networks, whch s based on dynamcally tunng HO margns. Two dfferent optmzaton approaches usng the fuzzy Q-Learnng algorthm have been nvestgated. UEE approach s based on an optmzaton scheme that explores all the canddate FLC actons throughout the load balancng process. BEE s an optmzaton scheme that combnes both explotaton and exploraton to enhance performance whle fndng the optmal FLC actons and also to provde dynamc adaptaton to system varatons. The UEE optmzaton approach s a useful method to accurately preserve the call qualty constrant, durng the load balancng by smply adjustng a call droppng threshold. However n BEE optmzaton approach, the FLC would select new optmal actons leadng to a lower value of CBR and speedng up the load balancng process whle preservng the same constrant n CDR. WANG Mn et al [8] have proposed a mn-max load balancng (LB) scheme to mnmze the demanded rado resources of the maxmum loaded cell. For the mxed multcast and uncast servces, multcast servces are transmtted by sngle frequency network (SFN) mode and uncast servces are delvered wth pont-to-pont (PTP) mode. The mn-max LB takes nto account pontto-multpont (PTM) mode for multcast servces and selects the proper transmsson mode between SFN and PTM for each multcast servce to mnmze the demanded rado resources of the maxmum loaded cell. The proposed mn-max LB scheme requres less rado resources from the maxmum loaded cell than SFN mode for all multcast servces. However the rado resource consumpton ncreases. Mng L et al [9] have proposed an LTE vrtualzaton framework (that enables spectrum sharng) and a dynamc load balancng scheme for mult-enb and mult-vo (Vrtual Operator) systems. They also nvestgate the parameterzaton of both schemes, e.g. sharng ntervals, LB ntervals and safety margns, n order to fnd the optmal parameter settngs. The LTE networks can beneft from both NV and LB technques. Pablo Muñoz et al [10] have desgned several load balancng technques based on self-tunng of femtocell parameters. In partcular, these technques are mplemented by fuzzy logc controllers (FLC) and fuzzy rule-based renforcement learnng systems (FRLSs). Performance assessment s carred out n a dynamc system-level smulator. The combnaton of FLC and FRLS produces an ncrease n performance that s sgnfcantly hgher than f technques are mplemented alone. Both the response tme and the fnal value of performance ndcators are mproved. 3. GAME THEORY AND FUZZY BASED LOAD BALANCING TECHNIQUE 3.1 Overvew In ths paper, we propose to desgn a game theory and fuzzy logc based load balancng technque for LTE networks. In ths technque, the load balancng s trggered based on the status of each cell whch s estmated usng fuzzy logc. Here the metrcs call blockng rato, transmt power, composte avalable and mssng capacty are consdered as nput for the fuzzy logc and the status of cell s determned as output. Based on the cell status, the load balancng s trggered and the dynamc hysteress adjustment s performed based on the game theory model. Here a utlty functon s formed n terms of RLF rato and the hysteress s dynamcally adjusted such that the utlty s maxmum. 354

4 3.2 Estmaton of Metrcs CBR Dfference: The Call Blockng Rato (CBR) s referred as the performed ndcator lnked to the call accessblty. It s estmated usng the followng equaton (1) CBR = z z b b = (1) o z b z + z Where z b = number of calls whch are blocked by the admsson control z a = number of calls whch are accepted by the admsson control z o = number of offered calls a CBR dfference s obtaned between the two adjacent cells ( and j) whch s used to balance the traffc among the cells. j CBR dff (t) = CBR (t) CBR j (t) (2) Transmt Power Dfference (P tx ) : The devaton n the P tx value s obtaned by comparng wth reference value as follows: P tx (t) = P tx (t) - P ref (t) P ref (t) = pre-defned reference power value. (3) Composte Avalable and Mssng Capacty: The composte avalable capacty of the cell (C c ) s estmated usng the followng equaton (4) where L (t) = C c = 100. q ( t) bw L ( t) 1 L trg L( t) = (1 β). L( t 1) + β. L( t) t where L ( ) = cell load L (t) = sample value of the load (4) q (t) = amount of occuped resources at the measurement nterval t. bw= total bandwdth of the cell (n terms of physcal resource blocks (PRBs) β = flter memory L trg = target operatonal load n terms of resources occupancy The composte mssng capacty (C m ) of the actve cell can be estmated usng the followng equaton (5) C m = 100. L ( t) L 1 Ltrg 3.3 Fuzzy based Cell Status Estmaton trg (5) We can trgger the load balancng based on the status of each cell whch s estmated usng fuzzy logc. Here the metrcs call blockng rato dfference, power dfference, composte avalable and mssng capacty (estmated n secton to 3.2.3) are provded as the nput to the fuzzy logc model and fuzzy decson rules are formed. Based on the outcome of the rules, the status of cell s decded. The steps that determne the fuzzy rule based nterference are as follows. Fuzzfcaton: Ths nvolves obtanng the crsp nputs from the selected nput varables and estmatng the degree to whch the nputs belong to each of the sutable fuzzy set. Rule Evaluaton: The fuzzfed nputs are taken and appled to the antecedents of the fuzzy rules. It s then appled to the consequent membershp functon. Aggregaton of the rule outputs: Ths nvolves mergng of the output of all rules. Defuzzfcaton: The merged output of the aggregate output fuzzy set s the nput for the defuzzfcaton process and a sngle crsp number s obtaned as output. The fuzzy nference system s llustrated usng fg

5 Fgure 3 Membershp Functon Of Transmt Power Fg 1 Fuzzy Inference System Fuzzfcaton: Ths nvolves fuzzfcaton of nput varables such as Call Blockng Rato (C), Transmt Power (P), Composte Avalable Capacty (A) and Composte Mssng Capacty (M) (Estmated n secton 3.2) and these nputs are gven a degree to approprate fuzzy sets. The crsp nputs are combnaton of C, P, A and M. We take three possbltes, hgh, medum and low for C, P, A and M. Fgure 2, 3, 4, 5 and 6 shows the membershp functon for the nput and output varables. Due to the computatonal effcency and uncomplcated formulas, the trangulaton functons are utlzed whch are wdely utlzed n real-tme applcatons. Also a postve mpact s offered by ths desgn of membershp functon. Fgure 4 Membershp Functon Of Composte Avalable Capacty Fgure 5 Membershp Functon Of Composte Mssng Capacty Fgure 2 Membershp Functon Of Call Blockng Rato Dfference Fgure 6 Membershp Functon Of Cell Status 356

6 In table 2, C, P, A and M are gven as nputs and the output represents the Cell Status. (S) S 0 to S 1 -> the cell remans n passve status - The passve cell status reveals that there s mnmal load. S1 to S 2 - > the cell remans neutral - The neutral cells does not partcpate n any load balancng actvty S 2 to S 3 and above - > the cell becomes actve - The actve cell status reveals that there s hgh load and the cells actvely partcpate n load balancng. The fuzzy sets are defned wth the combnatons presented n table 2. S. N o Call Bloc kng Rato Dffe rence (C) Tra nsm t Pow er (P) Com post e Aval able Capa cty (A) Com post e Mss ng Capa cty (M) Ce ll Sta tus (S) 1 Low Low Low Low S 0 2 Low Low Low Hgh S 0 3 Low Low Hgh Low S 0 4 Low Low Hgh Hgh S 1 5 Low Hgh Low Low S 3 6 Low Hgh Low Hgh S 2 7 Low Hgh Hgh Low S 4 8 Low Hgh Hgh Hgh S 2 9 Hgh Low Low Low S 2 10 Hgh Low Low Hgh S 2 11 Hgh Low Hgh Low S 3 12 Hgh Low Hgh Hgh S 2 S. N o Call Bloc kng Rato Dffe rence (C) Tra nsm t Pow er (P) Com post e Aval able Capa cty (A) Com post e Mss ng Capa cty (M) Ce ll Sta tus (S) 14 Hgh Hgh Low Hgh S 2 15 Hgh Hgh Hgh Low S 3 16 Hgh Hgh Hgh Hgh S 3 Table 2 demonstrates the desgned fuzzy nference system. Ths llustrates the functon of the nference engne and method by whch the outputs of each rule are combned to generate the fuzzy decson. For example Let us consder Rule 7 If (C = Low, P & A = Hgh, M = Low) Then End f Status of Cell s Actve (S 4 ) Defuzzfcaton: The technque by whch a crsp values s extracted from a fuzzy set as a representaton value s referred to as defuzzfcaton. The centrod of area scheme s taken nto consderaton for defuzzfcaton durng fuzzy decson makng process. The formula (6) descrbes the defuzzfer method. Fuzzy cost = [ allrules f *ψ (f )]/ ( f ) [ allrules ψ ] (6) Where fuzzy cost s used to specfy the degree of decson makng, f s the fuzzy all rules, and varable and ψ f ) s ts membershp functon. ( The output of the fuzzy cost functon s modfed to crsp value as per ths defuzzfcaton method. 13 Hgh Hgh Low Low S 3 357

7 3.4 Game Theory Based Load Balancng Technque Based on the cell status (estmated n secton 3.3), the load balancng s trggered and the dynamc hysteress adjustment s performed based on the game theory model. Here a utlty functon s formed n terms of Rado Lnk Falure (RLF) rato and the hysteress s dynamcally adjusted such that the utlty s maxmum. The game theory based load balancng technque s modeled by defnng the players, the utlty functon and the possble strateges. Let N 1 be the actve cell whch has excess load. Let N 2 be the passve cell whch has mnmum load Let a 1 be the lowest level at whch rado lnk falure rato s acceptable Let a 2 be the offset of a 1 rangng from 0 to a 1 The players N 1 and N 2 of the game are opposte locaton and ready for gamng. It nvolves the followng steps: 1. The game starts at tme t 2. The rado lnk falure s trggered durng servce nterrupton. 3. If RLF<(a 1 -a 2 ), Then Else The load balancng s trggered If (a 1 -a 2 ) <RLF <a 1, Then Hysteress value s adjusted The condton (1) represents decrease n RLF rato when compared to last adjustment. The condton (1) represents ncrease n RLF rato when compared to last adjustment. s represents the teratve adjustment 4. The utlty functon (UF ) for the game s the rado lnk falure (RLF) rato n the cell. UF for N 2 wth a rato a 1 and UF 0 s defned usng followng equaton UFN2 + z UF + z 1 a +, f 0 a 1 UF N2 =, N2 z = A j 1 j 1 otherwse (7) where A j = approxmaton value of the RLF rato z = number of actve cells. 5. Based on value of a 1, N 2 performs the load balancng. 6. The utlty functon of the N 2 wth UF 0 > 0 users and a 1 >1 s estmated usng the followng equaton UF0 + z0 UF0 + z0 z 0 a1 + = A j 1, f 0 a 1 UF 0 =, 1 otherwse Snce the network load s already defned, N 2 easly selects a 2 whch maxmzes the utlty functon by adjustng the hysteress value. 4. SIMULATION RESULTS 4.1 Smulaton Parameters j End f Hysteress H = H( 1) + s H( 1) s (1) (2) We use NS2 [12] to smulate our proposed Game Theory and Fuzzy Based Load Balancng Technque (GFLBT) protocol (FLB, GLB). In our smulaton, the packet sendng rate s vared as 1, 1.5, 2, 2.5 and 3Mb. The area sze s 1200 meter x 1200 meter square regon for 50 seconds 358

8 smulaton tme. The smulated traffc s Vdeo and Exponental (Exp). Our smulaton settngs and parameters are summarzed n table 1 No. of Nodes 31 Table 1: Smulaton Parameters Area 1200 X 1200 Smulaton Tme 50 sec Traffc Source Vdeo and Exp Rate Propagaton Antenna Intal Energy 4.1J Transmsson Power Recevng Power 1,1.5,2,2.5 and 3Mb Two Ray Ground Omn Antenna Average end-to-end delay: The end-to-enddelay s averaged over all survvng data packets from the sources to the destnatons. Throughput: The throughput s the amount of data that can be sent from the sources to the destnaton. Bandwdth: It s the number of mega bts receved by the recever. 4.3 Results & Analyss The smulaton results are presented n the next secton. Comparson of FLBT and GFLBT: To analyze the performance load balancng technque by usng fuzzy logc and wthout usng game theory, In ths secton, the proposed Game theory and Fuzzy based Load Balancng Technque (GFLBT) s compared wth Fuzzy based Load Balancng Technque (FLBT). We vary the data sendng rate as 1, 1.5, 2, 2.5 and 3Mb for Exponental and vdeo traffcs Fgure 7 and 8 show the results of bandwdth and farness for FLBT and GFLBT technques by varyng the rate of Exponental traffc. When comparng the performance of the two protocols, we nfer that FLBT outperforms GLBT by 20% n terms of bandwdth and 14% n terms of farness. Smulaton Topology 4.2 Performance Metrcs Fg 7: Rate Vs Bandwdth For EXP We evaluate performance of the new protocol manly accordng to the followng parameters. Average Packet Delvery Rato: It s the rato of the number of packets receved successfully and the total number of packets transmtted. 359

9 wth Game theory based Load Balancng Technque (GLBT). The data rate s vared as 1, 1.5, 2, 2.5 and 3Mb for Exponental and Vdeo traffc. Fg 8: Rate Vs Farness For EXP Fg 11: Rate Vs Bandwdth For EXP Fg 9: Rate Vs Bandwdth For Vdeo Fg 12: Rate Vs Farness for EXP Fg 10: Rate Vs Farness For Vdeo Fgure 9 and 10 show the results of bandwdth and farness for FLBT and GFLBT technques by varyng the rate of vdeo traffc. When comparng the performance of the two protocols, we nfer that FLBT outperforms GLBT by 28% n terms of bandwdth and 78% n terms of farness. Fg 13: Rate Vs Bandwdth For Vdeo Comparson of GLBT and GFLBT: To analyze the performance load balancng technque by usng game theory model and wthout usng fuzzy logc, In ths secton, the proposed GFLBT s compared 360

10 Fg 14: Rate Vs Farness For Vdeo Fg 16: Rate Vs Delvery Rato Fgure 11 and 12 show the results of bandwdth and farness for GFLBT and GLBT technques by varyng the rate of Exponental traffc. When comparng the performance of the two protocols, we nfer that GFLBT outperforms GLBT by 41% n terrms of bandwdth and 28% n terms of farness. Fgure 13 and 14 show the results of bandwdth and farness for GFLBT and GLBT technques by varyng the rate of vdeo traffc. When comparng the performance of the two protocols, we nfer that FLBT outperforms GLBT by 28% n terrms of bandwdth and 78% n terms of farness. Fg 17: Rate Vs Bandwdth Comparson of DHA and GFLBT: In ths secton, we compare the dynamc hysteress adjustment (DHA) [4] protocol wth the proposed GFLBT protocol. The performance s measured by varyng the rate for both Exponental and Vdeo traffc. Case-1 Exponental Traffc The data sendng rate s vared as 1, 1.5, 2, 2.5 and 3Mb for Exponental traffc. Fg 18: Rate Vs Farness Fg 15: Rate Vs Delay 361

11 Fg 19: Rate Vs Throughput Fg 21: Rate Vs Delvery Rato Fgures 15 to 19 show the results of delay, delvery rato, bandwdth, farness and throughput by varyng the rate from1mb to 3Mb for the Exponental traffc n GFLBT and DHA protocols. When comparng the performance of the two protocols, we nfer that GFLBT outperforms DHA by 34% n terms of delay, 73% n terms of delvery rato, 33% n terms of bandwdth, 51% n terms of farness and 72% n terms of throughput. Case-2 Vdeo Traffc The data sendng rate s vared as 1, 1.5, 2, 2.5 and 3Mb for Vdeo traffc. Fg 22: Rate Vs Bandwdth Fg 20: Rate Vs Delay Fg 24: Rate Vs Farness 362

12 Fg 25: Rate Vs Throughput Fgures 20 to 25 show the results of delay, delvery rato, bandwdth, farness and throughput by varyng the rate from1mb to 3Mb for the Exponental traffc n GFLBT and DHA protocols. When comparng the performance of the two protocols, we nfer that GFLBT outperforms DHA by 15% n terms of delay, 71% n terms of delvery rato, 51% n terms of bandwdth, 76% n terms of farness and 71% n terms of throughput. 5. CONCLUSION In ths paper, we have proposed to desgn a game theory and fuzzy logc based load balancng technque for LTE networks. In ths technque, the load balancng s trggered based on the status of each cell whch s estmated usng fuzzy logc. Here the metrcs call blockng rato, transmt power, composte avalable and mssng capacty are consdered as nput for the fuzzy logc and the status of cell s determned as output. Based on the cell status, the load balancng s trggered and the dynamc hysteress adjustment s performed based on the game theory model. By smulaton results, we have shown that the proposed technque mnmzes the rado lnk falure. REFERENCES [1] LI Bo, WANG X-yuan and YANG Da-cheng, Inter-doman traffc balancng wth co-channel nterference management n mult-doman heterogeneous network for LTE-A, The Journal of Chna Unverstes of Posts and Telecommuncatons [2] Zhhang L, Hao Wang, Zhwen Pan, Nan Lu and Xaohu You, Dynamc Load Balancng n 3GPP LTE Mult-Cell Fractonal Frequency Reuse Networks, Vehcular Technology Conference (VTC Fall), 2012 IEEE. IEEE, [3] Ahmad Awada and Bernhard Wegmann, A Game-Theoretc Approach to Load Balancng n Cellular Rado Networks, Personal Indoor and Moble Rado Communcatons (PIMRC), 2010 IEEE 21st Internatonal Symposum on. IEEE, [4] L, Wenyu, et al. "A dynamc hysteressadjustng algorthm n LTE self-organzaton networks." Vehcular Technology Conference (VTC Sprng), 2012 IEEE 75th. IEEE, [5] Avlés, José M. Ruz, et al. "Analyss of load sharng technques n enterprse LTE femtocells." Wreless Advanced (WAd), IEEE, [6] Omar Altrad & Sam Muhadat, Load Balancng Based on Clusterng Methods for LTE Networks, Multdscplnary Journals n Scence and Technology, Journal of Selected Areas n Telecommuncatons (JSAT), February Edton, 2013 Volume 3, Issue 2. [7] Muñoz, P., Raquel Barco, and Isabel de la Bandera. "Optmzaton of load balancng usng fuzzy Q-learnng for next generaton wreless networks." Expert Systems wth Applcatons 40.4 (2013). [8] Mn, W. A. N. G., Chun-yan FENG, and Tanku ZHANG. "Mn-max load balancng scheme for mxed multcast and uncast servces n LTE networks." The Journal of Chna Unverstes of Posts and Telecommuncatons 19.2 (2012). [9] Mng L, Lang Zhao, X L, Xaona L, Yasr Zak, Andreas Tmm-Gel, Carmelta Görg, "Investgaton of network vrtualzaton and load balancng technques n LTE networks." Vehcular Technology Conference (VTC Sprng), 2012 IEEE 75th. IEEE, [10] Pablo Muñoz, Raquel Barco, José María Ruz- Avlés, Isabel de la Bandera, and Alejandro Agular, Fuzzy Rule-Based Renforcement Learnng for Load Balancng Technques n Enterprse LTE Femtocells, IEEE Transactons On Vehcular Technology, Vol. 62, No. 5, June [11] Panagots Fotads, Mchele Polgnano, Danela Laselva, Benny Vejlgaard, Preben Mogensen, Ralf Irmer and Nel Scully, Mult- Layer Moblty Load Balancng n a Heterogeneous LTE Network, Vehcular Technology Conference (VTC Fall), 2012 IEEE. IEEE, [12] Network Smulator: 363