Combined depth-first and breadth-first searching on partitioned tree for ML decoding of multiple input multiple output signals

Abstract

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