The Research Of Inter-cell Interference Suppression Techniques In Multi-base Station Cooperation Systems

Inter-cell interference in cellular mobile communication network is a bottleneck for promoting the communication system capacity, which can’t provide edge users available throughput to satisfy high rate requirement. Multi-base station cooperative(MBSC) communication has been proposed to suppress the negative effects of inter-cell interference, which enlarges the coverage of the signal and improves the transmission rate. Beamforming is an important technique to realize the interference suppression for MBSC communication. Focuses on the problem of beamforming in MBSC system, the major contributions are described as follow.Firstly, we introduce the related theory of inter-cell interference suppression for MBSC. We describe the inter-cell interference and its suppression technology. Moreover, the definition, classification and system models of MBSC communication are summarized.Secondly, a beamforming algorithm for MBSC based on game theory is proposed to suppress the inter-cell interference of the traditional cellular communication system, which will improve the system performance. The algorithm is an iterative algorithm for maximizing their own interests when the selected strategies of other cells are known. With this algorithm, the user rate maximization is formulated as a utility function optimization problem and solved it in its equivalent form. Moreover, the Nash Equilibrium existence and uniqueness of the algorithm are also proved. Moreover, the steps of solving Nash Equilibrium are also provided. Numerical simulations show that, compared with non-cooperative algorithm, the proposed algorithm is capable of nulling interfering users and reducing the inter-cell interference with a good convergence.Thirdly, to improve the transmission rate of MBSC communication system, a beamforming strategy for MBSC based on linearly-decrease inertia weight particle swarm optimization(LIWPSO) algorithm is presented, where the sum rate of the system maximization is formulated as a fitness function optimization problem. In order to avoid falling into local optimal in search process, the inertia weight of the algorithm is adopted linear decreasing approach. Moreover, the construction method of fitness function is also obtained according to the optimization problem. Numerical simulations show that compared with particle swarm optimization, the proposed algorithm will increase the sum rate greatly with a good convergence.