A low-complexity and near-optimal massive MIMO detection algorithm

In order to improve the performance and reduce the complexity of Massive Multiple-input Multiple-output (MIMO) detection algorithm, a low-complexity near-optimal detection algorithm based on continuous replacement Richardson iteration is proposed. The algorithm adopts a continuous replacement strategy to improve the convergence speed of traditional Richardson iterations. In addition, an initialization strategy based on eigenvalue estimation is proposed, which further improves the performance of the algorithm at a low complexity. Simulation results show that the algorithm has a significant improvement over traditional Richardson iteration performance. In a 128×16 scale MIMO system, when the number of iterations is 2 and the bit error rate is 10^-4, there is only 0.06 dB performance losscompared to MMSE's, and 2 dB performance improvement compared to Jacobi's, while the algorithm complexity is reduced by 10.8%. The article also gives the hardware implementation results of the algorithm on the Xilinx Virtex-7 FPGA platform, which has a higher throughput rate than other algorithms, reaching 10.3 Mbps.