| As a novel interference management technique, interference alignment (IA) can effectively improve the system capacity of wireless communication networks. So IA is of great promise to interference management, which may also have important theoretical and practical significance.On the basis of investigating the basic principles of IA for multi-user MIMO system, this thesis has made some innovative achievements as follows.In order to overcome the drawbacks of distributed IA algorithms for MIMO interference channels (IC), an IA algorithm based on the exacted potential game is proposed. By constructing a bounded potential function, the proposed game is proved to be a bounded exact potential game which may converge to the ε-Nash Equilibrium point after finite iterations. The impacts of cost factor on the performance of algorithms are also discussed. The new algorithm achieves better performance than the existing Min-INL and Max-SINR IA algorithms and significantly improves the MIMO interference channel’s capacity.A novel IA technique for downlink cellular networks is proposed in view of improving the sum capacity performance of per cell. A downlink IA mathematics model for cellular networks is constructed. The receive filter vector is optimised to maximize SINR at the receivers, while the precoder matrix is adapted via a gradient projection method to maximize the sum capacity of per cell at base-stations. Numerical results demonstrate that the new algorithm achieves better capacity performance than classic downlink IA algorithms for cellular networks. Aiming to obtain the global channel state information (CSI), we first proposed a new codebook algorithm based on a comprehensive learning partical swarm optimization (CLPSO) to reduce the complexcity of codebook construction and, simuteniously, improve the performance of codebook and thereby enhance the learning ablility and the convergence rate via the dynamic adjustment the velocity of particles. Then, a new hierarchical codebook design scheme is proposed to further reduce the storage requirement and computation complexity. Based on the scaling and rotation operations on vector, the observation is rotated to match the canonical local codebook. Finally, a new distributed IA (DIA) algorithm is proposed based on the Grassmannian codebook to further reduce the overhead of the precoding matrix for interference channel without centralized controller, precoding matrices and receive matrices are obtained from Grassmannian codebook, which are optimized aim to minimize the interference leakage. Compared with the perfect feedback, the proposed algorithm can achieve the near-optimal throughput while with much less feedback overhead.In order to solve the limitation of impractical assumption of symmetric path loss of all interference links, a novel partial IA scheme, which requires relatively small amounts of antennas and, therefore, can be practically implemented, is proposed to enhance the throughput of multi-cell networks by taking the heterogeneous path losses into full considerations. The partial IA problem is formulated as a mixed integer bi-level optimal program, which is decomposed into two sub-problems inorder to reduce the computation complexity. Furthermore, two algorithms are introduced for the interference link selection. It is shown that, our proposed algorithm can achieve good performence with far less transmit antennas. Subsequently, we investigate the IA performence loss aroused by limited feedback, and optimize the receivers bit allocation to decrease the leakage interference by exploiting the advantage of heterogeneous path loss. Simulation results show that the throuthput loss is reduced by dynamic feedback bit allocation Finally, the content of the whole dissertation is summarized, and several valuable research directions of IA Multi-user MIMO system are discussed. |
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