Heterogeneous cellular networks composed of small cells and macrocells can both improve the quality of service in hot spots and make up for blind spots that can not be covered by macro base stations and extend the coverage area,which is currently a hot research topic in 5G technology.Moreover,heterogeneous cellular networks can improve the efficiency through spectrum sharing.However,the intensive deployment of small cells has brought some new problems and challenges,such as low efficiency of resource allocation,severe interference in the same layer,and so on.This thesis mainly studies the resource allocation algorithms in heterogeneous cellular networks based on game theory and analyzes the access control,energy efficiency optimization and power allocation of small cells.For the uplink of heterogeneous network composed of macro base station and small base stations,this thesis proposes an access mechanism based on coalition game in small cell networks.Firstly,the time division multiplexing method is defined in each coalition to share the spectrum and reduce the interference in the coalition.Then,according to the interference characteristics of the coalition game,the achievable transmission rate of each user is calculated,and the various utility values in the coalition game are also defined.Finally,under the constraint of the threshold of the access signal-to-noise ratio,the access optimization problem is constructed to obtain the optimal system rate,and an access algorithm based on coalition game in small cell networks is proposed to solve the optimization problem.The results show that the proposed algorithm can converge to a stable value,and the system rate of this algorithm is superior in the area with densely distributed users.For the energy efficiency optimization in the co-channel deployment of small cell networks,this thesis proposes a distributed game algorithm based on joint power allocation and coalition formation.First,the scenario of coalition formation game is constructed to share the spectrum of the small cells in the same coalition by time division multiplexing.Then,the transmission rate and energy efficiency of the users in the small cell are defined,and the number of bits passed by each Joule is used as an indicator of energy efficiency.In addition,the optimization problem of joint power distribution and coalition formation with the goal of maximizing the energy efficiency of the system is established.In the process of coalition formation game,we combine the iterative solution based on non-cooperative game to solve the joint optimization problem,and get the optimal power distribution and the maximum system energy efficiency under the current coalition structure.At the same time,the small cell takes the system energy efficiency as a criterion for the transfer between the coalitions to maximize the system energy efficiency.The simulation shows that after many games,the coalition structure will finally reach a steady state,and simultaneously it will also obtain the largest system energy efficiency and the corresponding optimal power allocation.For the downlink energy-harvesting small cell network,this thesis proposes an interference management algorithm based on distributed coalitional game.The cooperative interference management problem of the energy-harvesting small cells is modeled as a coalitional game with transfer utility and the time sharing mode of the small cells in the same coalition is determined based on the energy harvesting strategy of the small cells.Then,an optimization problem is constructed to maximize the total system capacity of the energy-harvesting small cells.By using the distributed algorithm for coalition formation proposed in this thesis,the stable coalition structure,optimal time sharing strategy and optimal power distribution are found to maximize the total utility of the small cell system.The performance of the proposed algorithm is discussed and analyzed finally,and it is proved that this algorithm can converge to a stable coalition structure with reasonable complexity.The simulations show that the total system rate of the proposed algorithm is superior to that of the non-cooperative algorithm in the case of dense deployment of small cells,and the proposed algorithm can converge quickly. |