Randomized lattice decoding: Bridging the gap between lattice reduction and sphere decoding

Abstract

Sphere decoding achieves maximum-likelihood (ML) performance at the cost of exponential complexity; lattice reduction-aided decoding significantly reduces the decoding complexity, but exhibits a widening gap to ML performance as the dimension increases. To bridge the gap between them, this paper presents randomized lattice decoding based on Klein's randomized algorithm, which is a randomized version of Babai's nearest plane algorithm. The technical contribution of this paper is two-fold: we analyze and optimize the performance of randomized lattice decoding resulting in reduced decoding complexity, and propose a very efficient implementation of random rounding. Simulation results demonstrate near-ML performance achieved by a moderate number of calls, when the dimension is not too large.