Computer Science – Information Theory
Scientific paper
2010-02-27
Computer Science
Information Theory
23 pages with 7 figures
Scientific paper
Sphere decoding achieves maximum-likelihood (ML) performance at the cost of exponential complexity; lattice reduction-aided successive interference cancelation (SIC) 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 sampling technique, which is a randomized version of Babai's nearest plane algorithm (i.e., SIC). To find the closest lattice point, Klein's algorithm is used to sample some lattice points and the closest among those samples is chosen. Lattice reduction increases the probability of finding the closest lattice point, and only needs to be run once during pre-processing. Further, the sampling can operate very efficiently in parallel. 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 samples, when the dimension is not too large. Compared to existing decoders, a salient feature of randomized lattice decoding is that it will sample a closer lattice point with higher probability. A byproduct is that boundary errors for finite constellations can be partially compensated if we discard the samples falling outside of the constellation.
Ling Clifton C.
Liu Shuiyin
Stehlé Damien
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