Approximate iteration detection with iterative refinement in massive MIMO systems

To improve energy efficiency and spectral efficiency, massive multiple-input-multiple-output (MIMO) is proposed and becomes a promising technology in the next generation mobile communication. However, massive MIMO systems equip with scores of or hundreds of antennas which induce large-scale matrix computations with tremendous complexity, especially for matrix inversion in data detection. Thus, many detection methods have been proposed using approximate matrix inversion algorithms, which satisfy the demand of precision with low complexity. In this study, the authors focus on the approximate detection method based on Newton iteration (NI), and propose upgraded methods named NI method with iterative refinement (NIIR) and diagonal band NIIR (DBNIIR) which combine NI method and DBNI method with iterative refinement (IR). The results show that their proposals provide about 2 dB improvement on bit error rate (BER) for 16-quadrature amplitude modulation (QAM), and could even break the error floor existing in NI and DBNI methods for 64-QAM modulation. Furthermore, the BER of their proposals could provide almost the same performance as the exact method. Moreover, in contrast with NI and DBNI methods, NIIR and DBNIIR methods require quite few extra complexity cost and no extra hardware resource which is quite suitable for data detection in massive MIMO.