Sphere decoding with a probabilistic tree pruning

Abstract

In this paper, we present a near ML-achieving sphere decoding algorithm that reduces the number of search operations in the sphere-constrained search. Specifically, by adding a probabilistic noise constraint on top of the sphere constraint, a more stringent necessary condition is provided, particularly at an early stage, and, hence, branches unlikely to be survived are removed in the early stage of sphere search. The tradeoff between the performance and complexity is easily controlled by a single parameter, so-called pruning probability. Through the analysis and simulations, we show that the complexity reduction is significant while maintaining the negligible performance degradation.