DISTRIBUTED LEARNING FOR ENERGY-EFfiCIENT RESOURCE MANAGEMENT IN SELF-ORGANIZING HETEROGENEOUS NETWORKS
ABSTRACT
In heterogeneous networks, a dense deployment ofbase stations (BSs) leads to increased total energy consumption,and consequently increased co-channel interference (CCI). Inthis paper, to deal with this problem, self-organizing mechanismsare proposed for joint channel and power allocation procedureswhich are performed in a fully distributed manner. A dynamicchannel allocation mechanism is proposed, in which the problemis modeled as a noncooperative game, and a no-regret learningalgorithm is applied for solving the game. In order to improve theaccuracy and reduce the effect of shadowing, we propose anotherchannel allocation algorithm executed at each user equipment(UE). In this algorithm, each UE reports the channel withminimum CCI to its associated BS. Then, the BS selects itschannel based on these received reports. To combat the energyconsumption problem, BSs choose their transmission power byemploying an ON-OFF switching scheme. Simulation results showthat the proposed mechanism, which is based on the second proposedchannel allocation algorithm and combined with the ONOFFswitching scheme, balances load a BSs. Furthermore,it yields significant performance gains up to about 40:3%, 44:8%,and 70:6% in terms of average energy consumption, UE’s rate,and BS’s load, respectively, compared to a benchmark based onan interference-aware dynamic channel allocation algorithm
EXISTING SYSTEM:
Several new techniques have been proposed to enhance theEE of HetNets via various approaches such as sleep modeand cell breathing, a user equipment(UE) association mechanism for sleeping cell UEs based onmaximum mean channel access probability is proposed. Theproposed mechanism adapts to traffic load at BSs, receivedsignal power, and cumulative interference power. In, acooperative optimization problem for maximizing EE, subjectto minimum received signal strength with a set of clustersis considered. For solving the problem, each cluster employssimulated annealing search algorithm in a distributed manner.However, this work does not consider each BS’s load. Theauthors proposed a set of small cell driven, corenetwork driven, and UE driven sleep mode algorithms forSBSs in HetNets.In, a centralized sleep mode technique for HetNetsby exploiting the cooperation of cells is proposed. In ,a multi-objective optimization problem based on sleep modetechnique for an orthogonal frequency-division multiple access(OFDMA) based system is developed. In order to find thesolution, a genetic algorithm is applied. However, the proposedmechanisms in rely on centralized methods, andcome at the expense of knowing global information. In, arandom SBS ON-OFF switching scheme is introduced, wherethe UEs can delay their transmissions for a closer SBS tobecome available, thereby significantly improving
PROPOSED SYSTEM:
The main contribution of this paper is to introduce anovel framework for joint channel allocation and BS ON-OFFswitching problem in densely deployed HetNets. We proposetwo novel channel allocation schemes. The first scheme isimplemented at the level of BSs, allowing BSs to dynamicallychoose their channels, and adapt them to the network’sconditions. Since the channels themselves do not have theirown individual payoff functions or preferences, investigatingnoncooperative game is more suitable compared to a matchinggame with two-sided preferences. Moreover, in a matchinggame, the matching can require additional signaling betweentwo player sets which can lead to overhead in the design,and increase complexity. On the other hand, due to thedistributed nature of networks and the competition a BSs,we cast the problem as a noncooperative game. To solve thisgame, a novel distributed learning-based approach is used, inwhich the knowledge of received interference on each channelis needed for choosing the appropriate channel. The needfor this distributed learning solution is desirable as it hasseveral advantages. First, since BSs can autonomously selecttheir channels based on the environmental information aboutthe channels without information exchange, it can reduce thesignaling overhead in the network. Moreover, this informationis useful for BSs to select their strategies with better long-termperformance. Second, a distributed decision taken by a BS isindependent on the number of BSs in the network. Therefore,this approach is a suitable solution for densely deployedHetNets, and when the number of BSs varies over time, andthere is no centralized controller. Furthermore, centralizedapproaches rely on a single controller entity. If the controllerentity is compromised, then it can lead to failures at theentire network. Thus, this distributed approach can improvethe network’s robustness to failures and attacks.The second proposed scheme is implemented at the levelof UEs. In this algorithm, all UEs in the coverage area ofa BS choose their priority channels, and report them to theBS. After collecting all the reports, the BS decides to chooseits channel based on the reports received from its associatedUEs, using Gibbs sampling method. illustratesour two-tier network model based on BS ON-OFF switchingand channel assignment algorithm based on Gibbs samplingmethod.
CONCLUSION
In this paper, we have proposed two dynamic channelassignment mechanisms, i.e. DCA-LA and GUIA. Later, wehave combined them with a BS ON-OFF switching algorithmin order to reduce the energy consumption of the network.The proposed DCA-LA/ON-OFF switching algorithm usesa game-theoretic approach, in which each BS selects itschannel and power based on a no-regret learning algorithm.In GUIA/ON-OFF switching algorithm, BSs utilize someinformation from their associated UEs to improve the performanceof the network. Then, they select their channelsbased on a Gibbs-sampler. GUIA/ON-OFF switching algorithmbalances the load a BSs. Therefore, it improvessystem throughput, and consequently yields a better SE. Asa result, our proposed algorithm achieves both high EE andSE. The proposed algorithms are all executed in a distributedmanner. Simulation results have shown that GUIA/ON-OFFswitching algorithm provides a better performance over theother algorithms, and significantly outperforms them in termsof average energy consumption, average load, average BS’sutility, average throughput, average number of dropped UEs,and average UE’s rate.
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