A reduced complexity and latency for massive MIMO using parallel detection algorithm

Abstract

In recent years, a high speed data transmission system has been widely researched. In particular, massive MIMO is rapidly researched. Massive MIMO uses a large number of antennas with about 100 antenna elements. In the MIMO system, Maximum Likelihood Detection (MLD) provides the best bit error rate (BER). However, this detection method needs a huge computational complexity because it selects desired signal from all candidates of transmitted signal. Therefore, it is difficult to use MLD for a massive MIMO. On the other hand, MLD with QR decomposition and M-algorithm (QRM-MLD) has been proposed to reduce computational complexity. Although QRM-MLD provides a little worse BER than MLD, it can reduce the latency and the huge computational complexity. However, in despite of a small latency and low computational complexity, QRM-MLD causes long latency and large computational complexity when we consider a large number of antennas. In this paper, we propose a novel detection method for a massive MIMO using parallel detection with QRM-MLD to reduce the complexity and latency. The proposed scheme obtain R matrix after permutation of H matrix and QR decomposition. The R matrix is also eliminated by Gauss-Jordan elimination method. By using modified R matrix, the proposed method detect the transmitted signal using parallel detection. From the simulation results, the proposed scheme can achieve a reduced computational complexity and latency with a little degradation of the BER performance compared with the conventional method in 16 × 16 MIMO systems.