Efficient Statistical Pruning for Maximum Likelihood Decoding Radhika

In many communications problems, maximum-likelihood (ML) decoding reduces to finding the closest (skewed) lattice point in N-dimensions to a given point x E CN. In its full generality, this problem is known to he NP-complete and requires exponential complexity in iV. Recently, the expected complexity of the sphere decoder, a particular algorithm that solves the ML problem exactly, has been computed where it is shown that over a wide range of rates, SNRs and dimensions the expected complexity is polynomial in N. In this paper, we propose an algorithm that, for large N , offers substantial computational savings over the sphere decoder, while maintaining performance arbitrarily close to ML. The method is based on statistically pruning the search space. Simulations are presented to show the algorithm's performance and the computational savings relative to the sphere decoder.