Low-complexity Prediction Techniques of K-best Sphere Decoding for MIMO Systems

In multiple-input multiple output (MIMO) systems, maximum likelihood (ML) detection can provide good performance, however, exhaustively searching for the ML solution becomes infeasible as the number of antenna and constellation points increases. Thus ML detection is often realized by K-best sphere decoding algorithm. In this paper, two techniques to reduce the complexity of K-best algorithm while remaining an error probability similar to that of the ML detection is proposed. By the proposed K-best with predicted candidates approach, the computation complexity can be reduced. Moreover, the proposed adaptive K-best algorithm provides a means to determine the value K according the received signals. The simulation result shows that the reduction in the complexity of 64-best algorithm ranges from 48% to 85%, whereas the corresponding SNR degradation is maintained within 0.13dB and 1.1dB for a 64-QAM 4 × 4 MIMO system.