Approaching Maximum-Likelihood Performance with Reduced Complexity for a Double Space-Time Transmit Diversity System

Abstract

A reduced-complexity detector approaching maximum-likelihood (ML) detection performance is presented for the double space-time transmit diversity system. The proposed scheme exploits both the special structure of equivalent channel matrix and decision-feedback detection. This accounts for accomplishing near-ML or ML performance with significantly relieved computational loads. Moreover, to moderate the average complexity, several distance metric selection criteria are proposed. We can control performance and computational savings according to different distance metric selection rules. Numerical results show that the proposed detector requires significantly fewer computations than that of the Schnorr-Euchner sphere-decoding algorithm in terms of both the worst-case and the average complexity.