Adaptive low-complexity constellation-reduction aided detection in MIMO systems employing high-order modulation

The K-best detection algorithm with its diverse variations, which belong to a typical breadth-first type of quasi maximum-likelihood (ML) detection algorithms, have been close to implementation in realistic communications systems for its excellent bit error rate (BER) performance with reasonable computational complexity. However, when high-order modulation schemes are employed, the complexity for MIMO detection remains drastically high. In this paper, we propose an adaptive low-complexity constellation-reduction aided K-best detection algorithm for MIMO systems using 64QAM/256QAM modulation schemes. Our proposed algorithm can adaptively reduce the size of candidate constellation set in each detection layer, which differs from the conventional schemes using a fixed strategy, such that the complexity can be further decreased. Moreover, our algorithm adopts real-valued constellation set for constellation-reduction rather than the traditionally-used complex-valued set, achieving better tradeoff between the computational complexity and detection performance. Simulation results show that compared with existing algorithms, our proposed approaches have better BER performance as well as reducing the complexity.