Research Of Signal Detection Algorithm In Large-scale MIMO System

Multiple Input Multiple Output?MIMO?technology sets multiple antennas at the transmitter to transmit signals and sets multiple antennas at the receiver to receive signals.In MIMO system,spatial multiplexing gain and spatial diversity gain are used to improve the channel capacity and the stability of the system respectively.Massive MIMO features great improvement in spectral and energy efficiency,and it is widely recognized as a very promising technology for the 5^th generation?5G?wireless communication systems.The massive MIMO technique has played the most important role in 5G wireless communication.However,there are several challenges to be solved for practical massive MIMO systems,one of which is the low-complexity detection scheme with near-optimal performance.In massive MIMO system,the signal detection algorithm will be more complex when the dimension of MIMO channel increases greatly.The main work of this paper is the signal detection algorithm which is suitable for massive MIMO system and the research content is as follows:??1?We introduce the classical signal detection algorithms in the traditional MIMO system,including the optimal maximum likelihood detection algorithm,linear and nonlinear detection algorithm.The linear detection algorithm includes matched filter detection algorithm,minimum mean square error detection algorithm and zero forced detection algorithm.The nonlinear detection algorithm includes interference elimination detection algorithm,sphere decoding detection algorithm and the QR detection algorithm.The advantages and disadvantages of each algorithm are compared.?2?In order to reduce the complexity of signal detection,we study the MMSE detection algorithm based on Neumann series expansion and iterative solution.The main difficulty of traditional linear signal detection algorithm in massive MIMO system is the implementation of low complexity and the computational complexity of linear detection algorithm is too high due to the large scale matrix inversion.From the perspective of reducing complexity,the signal detection algorithms based Neumann series expansion and iterative solution to approximate matrix inverse are analyzed.Detection algorithms based on iterative solution include Richardson iteration,Jacobi iteration and Gauss-Seidel iteration.The feasibility of the above signal detection algorithms in massive MIMO system is analyzed by simulation.?3?Based on the soft detection requirements,an improved soft information log likelihood ratio calculation method is proposed.An improved hybrid iterative signal detection algorithm for massive MIMO systems is designed,and the influence of spatial correlation between antennas on the algorithm is analyzed.A hybrid iterative detection algorithm called SDGS based on steepest descent?SD?algorithm and Gauss-Seidel?GS?iteration can solve the problem of matrix inverse in minimum mean square error?MMSE?algorithm.Meanwhile,the SD algorithm has a good convergence direction,which speeds up convergence in the iteration.The soft decision detection of MIMO system is very important for improving the detection performance,and the soft-input-soft-output system involves the log likelihood ratio.Therefore,on the basis of SDGS algorithm an improved hybrid iterative algorithm is proposed to improve detection performance while keeping low complexity where the calculation of log likelihood ratio in soft decision is improved.