List Stack Detection with Reduced Search Space for MIMO Communication Systems

The interest in near-ML detection algorithms for multiple-input/multiple-output (MIMO) systems have always been high due to their drastic performance gain over suboptimal algorithms. Algorithms such as the M algorithm and the stack algorithm yields near-ML performance while only requiring a fraction of the computational complexity of an ML receiver. While the stack algorithm is computationally less intensive than the M algorithm for uncoded systems and hard decision decoding, the reverse is true for soft decision decoding as a candidate list is required to compute the soft metric. The M algorithm can easily generate a candidate list at no extra cost, the stack algorithm, on the other hand, would require more iterations to generate the list. In this paper, we propose a modified stack algorithm which have a lower computational complexity than a conventional one without sacrificing much of the performance of the algorithm