Fast Converging Gauss-Seidel Iterative Algorithm for Massive MIMO Systems

Signal detection in massive MIMO systems faces many challenges. The minimum mean square error (MMSE) approach for massive multiple-input multiple-output (MIMO) communications offer near to optimal recognition but require inverting the high-dimensional matrix. To tackle this issue, a Gauss-Seidel (GS) detector based on conjugate gradient and Jacobi iteration (CJ) joint processing (CJGS) is presented. In order to accelerate algorithm convergence, the signal is first initialized using the optimal initialization regime among the three options. Second, the signal is processed via the CJ Joint Processor. The pre-processed result is then sent to the GS detector. According to simulation results, in channels with varying correlation values, the suggested iterative scheme's BER is less than that of the GS and the improved iterative scheme based on GS. Furthermore, it can approach the BER performance of the MMSE detection algorithm with fewer iterations. The suggested technique has a computational complexity of O(U2), whereas the MMSE detection algorithm has a computational complexity of O(U3), where U is the number of users. For the same detection performance, the computational complexity of the proposed algorithm is an order of magnitude lower than that of MMSE. With fewer iterations, the proposed algorithm achieves a better balance between detection performance and computational complexity.

Automated summary

Signal detection in massive MIMO systems faces many challenges. This paper proposes a Gauss-Seidel detector based on conjugate gradient and Jacobi iteration, with three initialization options to accelerate algorithm convergence. The suggested scheme outperforms other schemes in terms of bit error rate (BER) and approaches the performance of the MMSE detection algorithm with fewer iterations.