(A) maximum likelihood detection algorithm based on the breadth-first searching for multiple input multiple output systems

Abstract

The sphere decoder (SD) has recently been proposed to perform maximum likelihood (ML) detection for multi-input multi-output systems by mapping the ML detection into an equivalent problem of searching for the closet point in lattices. In this thesis, taking a different look at the search problem for closet lattice points, we propose a novel breadth-first detector (BFD) exhibiting a significantly lower computational cost than the SD for a wide range of signal to noise ratios. We in addition introduce a simple tuning scheme which allows the BFD to have a performance-complexity trade-off property. Simulation results show that the BFD has the same bit error rate performance as the conventional ML detector while allowing lower computational complexity than the SD.