Box Constrained Optimization for Signal Detection in Mimo Channels

We develop a computationally efficient approximation of the maximum likelihood (ML) detector for 16 quadrature amplitude modulation (16-QAM) in multiple-input multiple-output (MIMO) systems. The detector is based on solving a convex relaxation of the ML problem by a box constrained optimization scheme. Simulation results in a random MIMO system show that this proposed approach outperforms the conventional decorrelator detector and is similar to the semidefinite relaxation (SDR) detector. However, we should note that the complexity of the proposed approach is less than that of those detectors. In the case of 8 antennas and 4 users, about 99% fewer computations are required when compared to the SDR and ML detectors.