Low-complexity Detection Algorithms Based on Matrix Partition for Massive MIMO

Massive Multiple-Input Multiple-Output (MIMO) is one of the key technologies in the fifth generation (5G) wireless communication for much higher throughput. However, current detection algorithms for massive MIMO suffer from large computational complexity. The Neumann series based approximated matrix inverse is a good tradeoff between detection performance and computational complexity. In this paper, compared to the traditional Neumann series based method, a matrix partition (MP) method is proposed to significantly reduce the number of required multiplications and additions while maintain comparable or even better detection performance. The presented MP method innovates the construction of pre-conditioner matrix and employs the Neumann series method in an intelligent way. Simulation results from a 128 x 16 massive MIMO system with 16-QAM modulation demonstrate that the proposed MP method can reduce the number of multiplications and additions by 68% and 70%, respectively.