关键词: 大规模多输入多输出, 矩阵求逆, 低复杂度, Neumann级数, Jacobi迭代, 软判决

Abstract: Based on Jacobi iterative method, a low-complexity detection algorithm which can circumvent the matrix inverse operation was proposed. The proposed algorithm was proved to be convergent when it is applied in the MMSE detection scheme, and it can achieve the same performance of the classical Neumann series expansion. Unlike conventional Neumann method with the number of iterations exceeding three (i ≥ 3), whose computational complexity is increasing to O(K³), the proposed algorithm can keep it consistently at O(K²) with arbitrary number of iterations. In order to employ that in soft decision, an approximated method was adopted to compute log-likelihood ratios for soft channel decoding. Simulations verify that the proposed algorithm can converge rapidly and achieve its performance quite close to that of the MMSE algorithm with only a small number of iterations.

Key words: massive multiple-input multiple-output, matrix inversion, low-complicity, Neumann series, Jacobi, soft decision