Reduced-Complexity Detection Algorithms for Systmes Using Multi-Element Arrays

The maximum-likehood (ML) algorithm is the optimum detection method for BLAST systems, employing transmit and receive antenna arrays. However, the ML complexity increases exponentially with the number of transmit antennas and the number of bits per modulation symbol. Therefore, true ML detection is often too complex to implement in real systems. An effective, reduced-complexity detection method has been suggested, using ordered successive interference cancellation. In this paper, we consider two other suboptimum techniques: channel-based adaptive group detection and multi-step reduced-constellation detection. The goal is to reduce the two forementioned complexity exponentials. The algorithms efficiently combine linear processing with local ML search. We limit the complexity by maintaining small ML searching areas, while maximizing the performance under the complexity constraint by ioptimizing the front-end linear processing and the selection of the search areas. The techniques can also be used in combination with the original BLAST detection algorithms to further improve the performance.