Early-Pruned K-Best Sphere Decoding Algorithm Based on Radius Constraints

A technique to prune the paths for K-best sphere decoding algorithm (SDA) based on radius constraint is presented. The proposed scheme preserves breadth-first searching nature, and the distinct radii for each decoding layer are theoretically derived from the system model with the noise statistics. In addition, based on the data range provided by the radius, a low complexity sorting strategy is proposed. The proposed method can apply to SDA with various path cost functions. Euclidean norm and sum of absolute difference are demonstrated in this paper. With SNR degradation less than 0.2 dB, more than 47% and 90% computation complexity can be reduced in 16-QAM and 64-QAM 4times4 MIMO detection, respectively.