Likelihood-Based Tree Search for Low Complexity Detection in Large MIMO Systems

Abstract

A recently reported result on large/massive multiple-input multiple-output (MIMO) detection shows the utility of the branch and bound (BB)-based tree search approach for this problem. We can consider strong branching for improving upon this approach. However, that will require the solution of a large number of quadratic programs (QPs). We propose a likelihood based branching criteria to reduce the number of QPs required to be solved. We combine this branching criteria with a node selection strategy to achieve a better error performance than the reported BB approach, that too at a lower computational complexity. Simulation results show that the proposed algorithm outperforms the available detection algorithms for large MIMO systems.