Joint Processing Technology In Broadband Mobile Communication System

With the increasing of mobile tenninal devices, users put forward higher requirements for the efficiency and stability of mobile communication systems. To meet the demand, 4G and 5G systems have adopted dense network. In the network, a large number of macro base stations and low power base stations are deployed, the transmitter and receiver are configured with multiple antennas. While the system performance can be improved, intensive network has some problems,such as base station cooperation, wireless resource allocation,interference suppression and so on. Joint processing technique can solve these problems. The beamforming technique, as one of the joint processing techniques, can suppress the mutual interference in the space domain and effectively enhance the transmission capacity of the mobile communication system. Therefore, in the dense network scenario, the study of beamforming joint processing technology is of great significance.At first, two types of joint processing techniques are introduced:signal to leakage plus noise ratio (SLNR) maximization scheme and sum-rate maximization scheme. The advantages and disadvantages of these two schemes are analyzed. Then, for the MIMO scenario with multiple base stations and multiple users, we propose a new metric named leakage sum-rate, and two joint optimization schemes are designed based on this metric, including joint optimization scheme of beamforming and joint optimization scheme of base station clustering and beamforming.Leakage sum-rate is defined as the logarithm of the determinant of useful signal matrix and leakage interference signal inverse matrix multiplication. Using leakage sum-rate as cost function, the leakage interference can be effectively reduced and the complexity can be reduced with less loss of system capacity. The joint optimization scheme of beamforming adopts leakage sum-rate as cost function, which is constrained by the maximum transmit power of the base station, that is,the constraint of l, norm of the beamforming vector. After transforming,this problem can be solved by iterative algorithm. For joint optimization scheme of base station clustering and beamforming, the cost function is also leakage sum-rate, and the penalty of l1 norm of the beamforming vector is introduced. The cost function is constrained by the maximum transmit power of the base station. The introduction of l1 norm makes the beamforming vector have group sparse structure. This problem can be solved by LASSO related algorithm.At last, the system performance and algorithm complexity of different schemes are compared by simulation. When using leakage sum-rate as evaluation index, the performance of the new scheme is better than that of the sum-rate maximization scheme, while using sum-rate as evaluation index, the new scheme is lower than the latter. At the same time, the computational complexity of the new scheme is lower than that of the sum-rate maximization scheme. Simulation results show that new schemes can make a good balance between system performance and computational complexity.