Research On The Signal Detection Algorithm Of Massive MIMO For Uplink

Nowadays,the demand of wireless communication in services is growing exponentially.Due to solve the problem limitation of spectrum resources,Multiple Input Multiple Output technology was put forward.Massive Multiple Input Multiple Output technology could lay aside large-scale antenna array on the Base Station side.The conveying rate and spectrum efficiency of wireless communication can be effectively improved without increasing spectrum resources.However,with the increase of antennas at both the receiver and the transceiver,the space channel environment tends to be more and more complex.When the transmitted signal pass code and modulate reaches receiver,the restoration of the transmitted signal faces great challenges.The traditional linear Minimum Mean Square Error algorithm could reach suboptimal detection performance.However,the computing complexity of the Minimum Mean Square Error algorithm could add in the pace with the advance of matrix dimension,which could cause difficulty of matrix inversion.In the meantime,it is essential that the Maximum Likelihood algorithm could traverse each received signal in the plication for check.With the magnitude of air wire to add,it makes the computational complexity of the ML algorithm add exponentially.The paper deeply studies the massive Multiple Input Multiple Output on uplink for signal algorithm based on the backdrop.The main contents of the paper are as below:Firstly,the paper presents a massive Multiple Input Multiple Output system for uplink in signal detection,analyzing the principles of Zero Forcing algorithm,Maximum Ratio Combing algorithm,Minimum Mean Square Error algorithm in linear algorithm and Successful Interference Cancellation algorithm,Maximum Likelihood algorithm in nonlinear algorithm.In order to verify the performance of Bit Error Ratio of several classical algorithms,MATLAB phantom software is employed to emulate.The results of emulation indicate that the Maximum Likelihood algorithm achieves minimum Bit Error Ratio value in total algorithms,of which detection function is best.The Bit Error Ratio curve of the nonlinear algorithm is closer to the best curve of Bit Error Ratio than the linear algorithm.It indicates that the detection performance of the nonlinear method is better to get original transmitted signal,and the detection performance under QAM modulation is better than QPSK modulation usually.Secondly,a hybrid iteration algorithm is proposed in linear detection algorithm,which would combine Conjugate Gradient algorithm and Damped Jacobi algorithm.Using the better search direction of characteristics of Conjugate Gradient algorithm,it could provide to Damped Jacobi algorithm at the beginning of signal detection.Chebyshev polynomial is used to remove the relaxation parameters of Damped Jacobi algorithm to expedite the speed of convergence of hybrid iteration algorithm and eliminate the detection signals of subsequence from the effect of relaxation parameters.Finally,the soft judgment is used to improve detection performance.The simulation results show that the Bit Error Ratio curve of the hybrid iteration algorithm is closer to the Bit Error Ratio curve of Minimum Mean Square Error algorithm than Damped Jacobi algorithm or Conjugate Gradient algorithm.When iteration increase,Bit Error Ratio of the hybrid iteration algorithm will decrease.When the number of times of iterations is three or four,the improvement of detection performance is no longer obvious and the convergence is fast.The complexity of computation of hybrid iteration algorithm is much lower than complexity of computation of Minimum Mean Square Error algorithm.Finally,a ML-D-OSIC algorithm based on Order Successive Interference Cancellation algorithm is proposed in the paper.In order to solve the problem of early error propagation,a predefined threshold is defined and given in advance.When the distance between the detection points and the constellation points is greater than the predefined threshold through the Maximum Likelihood algorithm selects the best symbol.In the same time,number of elimination layers are dynamically adjusted according to the traversal capacity.The hybrid iteration algorithm is used to detection in the remain layer.The simulation results show that the detection performance of ML-D-OSIC algorithm is better than that of hybrid iteration algorithm,Minimum Mean Square Error algorithm and Order Successive Interference Cancellation algorithm.The detection performance of ML-D-OSIC algorithm is better than OSIC algorithm and linear detection algorithm when the predefined threshold is smaller,and the complexity of the ML-D-OSIC algorithm is much lower than that of Maximum Likelihood algorithm.