A Low-Complexity Hybrid Iterative Signal Detection Algorithm for Massive MIMO

Massive multiple input multiple output (MIMO) technology has been become one of 5G's core technologies. However, conventional MMSE algorithm usually involves high complexity due to the required matrix inversion of large-size matrix, which make it hard to implement in realistic applications. Based on this situation, the paper presents a hybrid iterative algorithm for signal detection for unlink, which is the combination of adaptive Damped Jacobi (DJ) algorithm and conjugate gradient (CG) algorithm. CG algorithm is used to provide an effective search direction for adaptive DJ algorithm. At the same time, Chebyshev approach is used to accelerate convergence and the soft information can be approximately solved by using the bit likelihood ratio in the channel coding. The complexity of algorithm is quantitatively analyzed in the theory, and the bit error rate performances and convergence speeds of different detection algorithms with different decision methods are studied by simulation experiments. The simulation results show that the proposed algorithm can achieve near-optimal MMSE linear detection performance under the condition of low complexity and a faster convergence speed after only a small number of iterations.