Branch and Bound with M algorithm for near optimal MIMO detection with higher order QAM constellation

Abstract

For multiple-input multiple-output (MIMO) systems, the optimum maximum likelihood (ML) detection requires tremendous complexity as the number of antennas or modulation level increases. This paper proposes a new algorithm which attains the ML performance with significantly reduced complexity. The proposed scheme is based on combining Branch and Bound algorithm (which solves an integer quadratic programming (IQP) problem in each node of the search tree) with M-Algorithm (which chooses M reliable candidates nodes out of the available nodes, in each stage of the search tree, and retain them) and hence we call it BB-M algorithm. The basic idea is analogues to the conventional QRD-M that presented in the literature, but the internal procedures of the algorithm is different, as the proposed algorithm uses the IQP based on BB algorithm. Not just that but also to reach maximum likelihood (ML) performance, the M value in BB-M is less than M in QRD-M. Simulation results show that the proposed detection scheme provides comparable performance to the ML at small M with fixed complexity regardless of the SNR and the constellation size. Hence, it is a promising scheme for optimal and near optimal performance of MIMO systems when adopting higher order QAM constellations.