A Low Complexity Soft-output Data Detection Scheme Based on Jacobi Method for Massive MIMO Uplink Transmission

In massive multiple-input multiple-output (MIMO) systems, linear minimum mean-square error (MMSE) detection can achieve near-optimal performance. However, it suffers from high computational complexity due to the involvement of matrix inversion. This issue becomes severer when user number (U) and receive antenna number (B) increase. Existing approaches such as Neumann series expansion method, Gauss-Seidel and Jacobi methods, can partly address this issue by approaching the matrix inversion with matrix multiplications or solving linear equations with iterative methods, respectively. However, matrix multiplications and the initialization for iterative methods are still costly. In this paper, we propose a further improved Jacobi method based soft-output massive MIMO detection scheme. The contributions include the use of matrix-vector product and a new approach to compute the log likelihood ratio (LLR). By using the matrix-vector product, the overall computational complexity is reduced from O(B x U-2) to O(B x U). The new approach uses the noise-plus-interference (NPI) from the MMSE estimation, instead of using that from the first iteration. We then propose an approximation method to obtain the covariance of the NPI from MMSE estimation. Finally, we demonstrate through numerical simulations that the proposed scheme outperforms the existing schemes in terms of computational complexity and system bit error rate performance.