| With the increasing dependence on the mobile intelligent terminal,more and more wireless multimedia application has walked into people's vision,and there has been a qualitative leap in the demand for the wireless mobile data services.According to the statistics from the International Telecommunication Union,the energy consumption and the proportion of CO2 emissions of the mobile communication industry are increasing year by year.Therefore,how to decrease the network energy consumption and improve the network energy efficiency while guarantee the users' demand by means of the efficient utilization of the existing wireless spectrum resource have become key problems be solved for the next generation of wireless network optimization design.In order to meet the targets of 1000 times data rate improvement and 10 times energy consumption decrease of the next generation of wireless network,academia and industry have devoted much effort on researching their key technologies.The ultra dense network(UDN)deployed with a large number of Co-Channel and low power small cells under the coverage of macrocell to shorten the distance between the user and its associated base station and improve the users' quality of service and network throughput has become an important component of the next generation wireless network.However,because of the large-scale deployment of small cells,the Co-Channel interference becomes more and more complex and the energy consumption of the network increases tremendously,which limits the improvement of the energy efficiency.In this dissertation,the energy efficiency problem of UDN is deeply studied.The main contributions of the dissertation are listed as follows:1.Energy-efficient resource allocation and cell association algorithm for ultra dense networks.Aiming at improving the current studies which didn't consider the alteration of cell association,the resource allocation of macrocell and small cells,and the users' data rate requirement,this chapter investigates the energy-efficient resource allocation problem of the macrocells and picocells in UDN.Under the constraint of the users' quality of service,a network energy efficiency maximization problem by jointly optimizing the frequency resource partitioning of macrocells,transmission power and cell association bias of small cells is formulated.This problem is proven to be an NP-hard problem which cannot be worked out by direct methods in polynomial time.And then,considering the alteration of users' serving cell in the optimization process and the non-convexity of energy efficiency,an improved particle swarm optimization(IPSO)based resource allocation and cell association algorithm is employed to solve the proposed joint optimization problem.In order to avoid the existence of the infeasible solutions,we apply the penalty function and convert the formulated constraint optimization problem into the unconstraint optimization problem.In IPSO,Tent based chaotic mapping is introduced to update inertia weight and Metropolis based neighborhood mobile strategy of Simulated Annealing algorithm is introduced to update the global best solution.By introducing these two updating mechanisms,the convergence and the optimality of the proposed algorithm can be guaranteed.At last,simulation results reveal that,compared to the existing resource allocation algorithms,the proposed algorithm can obtain better energy efficiency and system throughput performance with lower energy consumption.2.Carrier aggregation based energy-efficient dynamic resource allocation algorithm for ultra dense network.Aiming at the problems of the low signal-interference and noise ratio of users in the positive cell-association bias area,the poorly arranged carrier configurations and the excessive network energy consumption,a novel dynamic resource allocation scheme based on carrier aggregation is proposed.By adjusting the carrier configurations and imposing an energy efficiency price function based on power control can highly improve the network energy efficiency.Simulation results show that,compared to the existing resource allocation schemes,at the same time of the promotion of the network throughput,the proposed scheme can reduce the network energy consumption and hence improve the network energy efficiency.The reason for the improvement of energy efficiency is the optimized configuration of the component carrier and the dynamic adjustment of the transmission power.3.Energy-efficient cell-association bias adjustment algorithm for UDN.Aiming at the problems of the network load imbalance,network throughput limitation and the network energy waste caused by traditional cell association algorithms,in this chapter,under the constraint of users' data rate,we propose a novel cell association adjustment scheme by optimizing the cell-association bias configuration which finally alters the cell association relationship,increases network throughput,turns off the extra small cells that have no users,and hence improve the network energy efficiency.Considering the non-convexity of the energy efficiency optimization problem and the coupled relationship between cell association and scheduling during the optimization process,it is difficult to achieve an optimal cell-association bias solution.In this chapter,we first propose an energy-efficient centralized Gibbs Sampling based cell-association bias adjustment(CGSCA)algorithm.In CGSCA,global information such as channel state information,cell association information,and network energy consumption information need to be collected.Then,considering the overhead of the message exchange and the implementation complexity for CGSCA to obtain the global information in UDN,we propose an energy-efficient distributed Gibbs Sampling based cell-association bias adjustment(DGSCA)algorithm with a lower message-exchange overhead and implementation complexity.At last,we analyze the implementation complexities of the proposed two algorithms and other existing algorithms.Simulation results show that,compared to other existing algorithms,CGSCA and DGSCA have faster convergence speed,and higher performance gain of the energy efficiency and throughput.4.Joint energy-efficient cell association and backhaul link optimization algorithm for ultra dense network.Aiming at the problem of backhaul link congestion caused by coordinated multiple points(CoMP)joint transmission(JT),in this chapter,we focus on the scenario of the constrained backhaul link data rate in UDN and handle the network energy efficiency optimization problem under CoMP JT.Different from existing works which assume the data rate of fronthaul link is less than that of the backhaul link,ignoring the resource allocation and energy consumption of backhaul link,this chapter considers the scenario of the constrained backhaul link data rate where the backhaul link data rate cannot meet the forward link data rate,and proposes a network energy efficiency optimization algorithm under CoMP JT by jointly optimizing the cell association and backhaul link resource allocation of the network.The sleep/on indicator,cell association and backhaul link data rate allocation are tightly coupled with the resource consumption of small cell and the backhaul link,which makes the closed-form of the optimal solution difficult to be obtained.Under such circumstance,in this chapter,decomposition method is utilized to convert the original energy efficiency maximization problem into two subproblems.The first subproblem is an energy consumption minimization problem under the users' data rate constraint,and the second subproblem is maximizing the minimum achievable energy efficiency.The first subproblem is resolved by a cell association adjustment algorithm based on the modified improved particle swarm optimization algorithm,and the second subproblem is resolved by a backhaul link data rate control algorithm based on linear fractional programming.Numerical simulations show that,compared to the existing algorithms,the proposed algorithm exhibits higher energy efficiency,higher backhaul link data rate and lower network energy consumption while guarantee the users' data rate requirement. |
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