Improved QRD-M Algorithm Based on Adaptive Threshold for MIMO Systems

Abstract

Multiple-input multiple-output (MIMO) has been considered as a promising technique due to the fact that it dramatically increases the system throughput. In MIMO systems, signal detection at the receiver is regarded as one of the challenging tasks. Maximum likelihood (ML) detection provides minimum bit error rate (BER) by searching all possible candidates, however, its computational complexity grows exponentially as the number of antennas or modulation level increases. In order to reduce the complexity, QR decomposition (QRD)-M algorithm was proposed. The QRD-M algorithm achieves near ML performance by selecting M candidates in each layer, however, its complexity is still high when the number of M is large. In this paper, we propose an adaptive M selection scheme in QRD-M algorithm which provides comparable performance to the conventional QRD-M algorithm with significantly low complexity. In the proposed detection algorithm, the number of survival candidate symbols is determined by using an adaptive threshold to exclude unreliable candidate symbols. In order to verify the proposed algorithm, we present the BER of the proposed algorithm compared with that of the conventional QRD-M algorithm and analyze the computational complexities of the proposed and conventional algorithms.