Improved MIMO detection based on successive tree approximations

This paper proposes an efficient high-performance detection algorithm for MIMO communication systems that is based on a sequence of optimal tree approximations of the Gaussian density of the unconstrained linear system. The finite-set constraint is then applied to obtain a cycle-free discrete distribution that is suitable for message-passing algorithms. The proposed GTA-SIC algorithm is iterative and is based on first decoding the most reliable symbol, then canceling its contribution and applying the message-passing decoding to the smaller system. The computational complexity of the proposed GTA-SIC algorithm and the MMSE-SIC are comparable. The significantly improved MIMO decoding performance of the algorithm proposed here compared to lattice-reduction aided MMSE-SIC is demonstrated on several examples of large MIMO systems with high-order QAM constellations.