A Statistical Pruning Strategy for Schnorr-Euchner Sphere Decoding

The high computational complexity of maximum likelihood (ML) decoding can impact many applications such as code division multiple access (CDMA) and multiple-input multiple-output (MIMO) systems. The sphere decoder (SD) as an efficient ML decoder has therefore received significant attention in the wireless research community. This letter presents a new statistical method to reduce the complexity of the Schnorr and Euchner sphere decoder (SESD). The method uses a set of bounds, which are computed using the conditional probability based on the minimum metric of the current solution. A lookup tabic for the bounds can be computed offline. The proposed method is effective for any number of antennas with complexity savings about 50% or more over the conventional SD approach.