A Weighted Gauss-Seidel Iterative Algorithm with Fast Convergence

Unacceptable amounts of computation can be generated in massive multiple-input multiple-output (MIMO) systems with minimum mean squared error (MMSE) detection at the received side. A weighted Gauss-Seidel iterative algorithm with fast convergence is launched. The proposed algorithm uses a mixture of Conjugate Gradient and Jacobi iterations to select the optimal search direction. Then weighting factor is used to accelerate the traditional Gauss-Seidel iterative algorithm. The results show that the detection capability of the scheme has been improved. The theoretical analysis verifies that the proposed algorithm has a lower computational complexity compared to the MMSE algorithm. After simulation analysis, the recommended algorithm can gain better convergence speed and BER function with fewer iterations. If user antennas setting values is similar to base station antennas, the proposed algorithm is significantly better.