Research On Multi-Antenna Wireless Communication System Signal Detection Key Technologies

Multi-antenna technology can make full use of space resources, and effectively improve the wireless communication system channel capacity and transmission rate, without additional bandwidth and transmit power, the full use of space resources. The traditional MIMO, as a typical multi-antenna system, is about to achieve its theoretical throughput limit. The Massive MIMO(or large-scale MIMO), witch will further tap the spectrum utilization efficiency and improve service capacity, has become the great potential key technology in the next generation of wireless communication systems, also named 5G, making the signal detection technique in multiple antennas system more important.We mainly focused on the signal detection issues in the MIMO and Massive MIMO system. Firstly, we introduce the model of modern wireless communication system, the key technologies in the next-generation wireless communications system, and analyze the channel model and the channel capacity witch is commonly used in multi-antenna system. Secondly, several commonly used detection algorithms are introduced. Traditional linear detection algorithms are mainly including matched filter detection algorithm(MF), zeroforcing detection algorithm(ZF) and minimum mean square error detection algorithm(MMSE), as well as an approximate detection algorithm based on Neumann series expansion, with lower complexity, wich applies to the number of base station antennas is far larger than the number of users. The nonlinear detection algorithms mainly introduced are the serial interference cancellation algorithm(SIC), as well as two local search algorithm: increased likelihood search algorithm(LAS) and dynamic tabu search algorithm(RTS), we also gives the implementation steps of each algorithms. Thereafter, is the complexity analysis and system performance simulation of these algorithms. We get the BER curves after thesee simulations, witch is carried out in different environments, then the applicability of these algorithms is discussed and analysised. The simulation results show that with the increase in the proportion between the number of base station antennas and users, linear and nonlinear detection algorithms performance much like each other, so in Massive MIMO system, just the simpler linear detection algorithms has already met the system’s requirements.The most important part of liner detection algorthim is matrix inversion, its complexity increases with the number of antennas. Next, based on the detection algorithm research, we carry out the the corresponding study of the key technology in the detection module(matrix inversion module). First we discussed several common matrix inversion algorithm, gives a detailed analysis of its complexity and applicability. For its FPGA implementation, we select the improved inverse algorithm based on cholesky-matridecomposition, wich is much suiatable for this system. The FPGA design and implementation is taking the relationship between the consumption of resources, the maximum clock frequency, pipeline, latency and parallelization into consideration. We carried out the RTL simulation and also test the design on the Xilinx Virtex-7 FPGA. Wherein the size of 4*4 matrix with full pipeline structure, delay is 66 cycle, the maximum clock frequency up to 418 MHz, throughput 104Minv/s; 16*16 and 8*8 by using a multiplexing structure, the maximum delay was 174 cycle and 490 cycle respectively, the maximum clock frequency more than 500 MHz.