Early-Pruning K-Best Sphere Decoder for MIMO Systems

Abstract

The sphere decoding algorithm has been used for maximum likelihood detection in MIMO systems, and the K-Best sphere decoding algorithm is proposed for MIMO detections for its fixed complexity and throughput. However, to achieve near-ML performance, the K needs to be sufficiently large, which leads to large computational complexity and power consumption in hardware implementation. In this paper, we have developed some efficient early-pruning schemes, which can eliminate the survival candidates that are unlikely to become ML solution at early stages. Therefore, the computational complexity and the power consumption can be significantly saved. The simulation results show that for the 4×4 64QAM MIMO system, totally 55% computational complexity (or power consumption) can be reduced by applying our proposed schemes.