Randomized Lattice Decoding

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...

• Type: Journal
• Year: 2010
• Where: CORR
• Authors: Shuiyin Liu, Cong Ling, Damien Stehlé
• Keywords: Closest Lattice Point, CORR 2010, Education, Lattice Point, Randomized