A Hybrid ML Decoding Scheme for Multiple Input Multiple Output Signals on Partitioned Tree

Abstract

In this paper, we propose a novel ML decoding scheme based on the combination of depth- and breadth-first search methods on a partitioned tree for multiple input multiple output (MIMO) systems. The proposed scheme first partitions the searching tree into several stages, each of which is then searched by a depth- or breadth-first search method, maximally exploiting the advantages of both the depth- and breadth-first search methods. Numerical results indicate that, when the depth- and breadth-first search algorithms are adopted appropriately, the proposed scheme exhibits substantially lower computational complexity than conventional ML decoders while maintaining the ML bit error performance.