Near maximum-likelihood detection with reduced-complexity for multiple-input single-output antenna systems

Maximum-likelihood (ML) detection is guaranteed to yield minimum probability of erroneous detection and is thus of great importance for both multiuser detection and space-time decoding. For multiple-input multiple-output (MIMO) antenna systems where the number of receive antennas is at least the number of signals multiplexed in spatial domain, ML detection can be done efficiently using sphere decoding or many suboptimal detectors are well known to have reasonable performance at low complexity. Nonetheless, it is much less understood on obtaining good detection at affordable complexity if there is less number of receive antenna than transmitted signals. In this paper, our aim is to develop an efficient decoding strategy that can achieve near-ML performance for multiple-input single-output (MISO) or generally asymmetric fat MIMO systems. Our method is based on the fact that geometrically, the ML point happens to be a point that is close to the decoding hyperplane in all directions. The fact that the point that is close in all directions is much less is used to devise an efficient decoding method that ensures exact-ML performance for MISO systems with real signaling. For complex constellations, it can be coped with by simple extension using the idea of sphere decoding. Simulation results demonstrate that significant complexity reduction can be obtained while achieving near-ML performance.