Study On Game Theory-based Distributed Resource Allocation In Heterogeneous Cellular Networks

To cater for the explosively increasing number of subscribers and demand of high-data-rate services, deploying Heterogeneous Cellular Networks(HCNs) consisting of a high density of small base stations(BSs), e.g., Femto/Picocell BSs and relay nodes(RNs), Micro BSs as well as Macro BSs has been regarded as one promising approach for future wireless networks. In HCNs, the existence of severe cross-tier interference and co-tier interference would seriously compromise the benefits promised by this novel network architecture. Therefore, it is extremely important to design efficient resource allocation strategies for HCNs to mitigate the effect of interference and further enhance the system performance.Nowadays, improving the system performance without introducing higher capital expenditure(CAPEX) and operating expenditure(OPEX) has been envisioned as one essential target for the next generation of mobile communication systems. In this light, the distributed resource allocation approach featured by self-configuration and self-management has been considered as one key technology for future HCNs. Meanwhile, it should be noted that, as a mathematical subject for analyzing the conflict and cooperation between autonomous agents, game theory has been widely applied as a potentially effective tool to design distributed resource allocation schemes in wireless communications. Based on the above, this dissertation mainly focuses on how to promote the system performance in terms of three key performance indexes for future wireless communications, i.e., overall capacity, user satisfaction and energy efficiency(EE) for data transmission, via designing game theory-based distributed resource allocation schemes. The main achievements and results of this dissertation are summarized as follows:1. We have proposed a completely distributed subchannel allocation strategy and further proved its efficiency for the improvement of the overall capacity. For the HCN adopting the orthogonal channel deployment where the cross-tier interference has been avoided, how to efficiently mitigate the co-tier interference among different Femtocells with subchannel allocation becomes the key issue for achieving higher system capacity. In the second section of this dissertation, we have formulated this problem as a Noncooperative Rate Maximization Game(NRMG) and then, developed a completely distributed subchannel allocation algorithm. Since there is no information interaction among autonomous agents when implementing this algorithm, it is appropriate for Femtocells who are deployed in an ad hoc fashion and have the backhaul link with very limited capacity, e.g., digital subscriber lines(DSL). Furthermore, it has been provedthat the Pareto-efficient strategy can be achieved by our proposed algorithm. More importantly, in contrast to available approaches, our method does not require that the converging point is one Nash equilibrium(NE) of the formulated game. In this light, the proposed algorithm can be adopted to achieve good performance in more general cases.2. We have designed a distributed transmission control scheme where both the subchannel allocation and power control have been jointly investigated and moreover, proved that the overall satisfaction of users can be efficiently improved by the devised strategy. For the HCN with orthogonal channel deployment, under considering the transmission rate requirements of users belonging to randomly deployed Femtocells, it is very challenging to develop an efficient resource allocation strategy to make the whole system have good performance. Such a distributed channel selection and power control problem has been addressed in the third section of this dissertation. First, based on the fact that, in general, the data rate requirements of all Femtocell Users(FUs) cannot be satisfied simultaneously, a sigmoid function has been used to measure the satisfaction(utility) of each FU and then, the concerned problem has been formulated as a Non-cooperative Transmission Control Game(NTCG). After that, we have developed a Utility-based Transmission Control algorithm(UTC), which is completely distributed since there is no information exchange among different Femtocells. Moreover, it has been proved that the globally optimal solution can be asymptotically achieved by the devised algorithm under the given condition. In addition, it has been shown that there is no requirement that the converging point is one NE of the formulated game. Compared with the available algorithm, ours has a faster convergence speed and meanwhile, can bring the higher user satisfaction as well as transmission capacity.3. We have devised a distributed joint subchannel and power resources allocation strategy and meanwhile, proved the efficiency of this strategy in terms of the energy efficiency(EE) for data transmission. When co-channel deployment is adopted in HCNs, cross-tier interference may occur. To guarantee the Qo S requirement of Macrocell Users(MUs) and achieve green wireless communications, it is therefore very necessary to design efficient resource allocation schemes to deal with the cross-tier interference and improve the EE of data transmission. In the fourth section of this dissertation we have focused on this scenario and formulated the Joint Subchannel Allocation and Power Control(JSAPC) problem as a multi-objective mixed integer non-convex programming with coupling constraints. To tackle this problem, we first relax the integer variable as a continuous one and then, formulate the relaxed problem as the Non-cooperative game. For the formulated game, a distributed algorithm has been devised, with which one Pareto-efficient NE can be achieved. After that, under the consideration of the fairness and efficiency issues, we have developed a distributed resource allocation algorithm todeal with the original problem with integer variables. With this developed algorithm, the JSAPC problem is transformed into a one-dimensional pricing factor profile searching problem, which reduces the computational complexity. Besides that, both the analysis and simulation results validate that with our algorithm, small cell users with different priorities could achieve differential performance by adjusting their own individual pricing factor increment step sizes.