Low complexity near-maximum likelihood decoding for MIMO systems

In recent literature, an increasing radii algorithm (IRA) is introduced to decode the signals in multiple-input multiple-output (MIMO) systems. It is developed from the idea of sphere decoder (SD) and can achieve near-maximum likelihood (ML) decoding performance with relatively lower complexity than the SD by taking the noise statistics into consideration. In this paper, an improved IRA (IIRA) is proposed to further reduce the complexity. Apart from the noise statistics, channel information is taken into consideration as well. Additionally, a radii update scheme is introduced which enables the search space of the proposed algorithm to be further pruned. As a result, the proposed algorithm achieves a performance close to that of the IRA but with substantial computational savings. The effectiveness of the proposed decoder is verified by simulations.