  Despite its reduced complexity, lattice reduction-aided decoding exhibits a
widening gap to maximum-likelihood (ML) performance as the dimension increases.
To improve its performance, 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., successive interference cancelation (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 decoding radius of sampling decoding resulting in
better error performance than Klein's original algorithm, and propose a very
efficient implementation of random rounding. Of particular interest is that a
fixed gain in the decoding radius compared to Babai's decoding can be achieved
at polynomial complexity. The proposed decoder is useful for moderate
dimensions where sphere decoding becomes computationally intensive, while
lattice reduction-aided decoding starts to suffer considerable loss. Simulation
results demonstrate near-ML performance is achieved by a moderate number of
samples, even if the dimension is as high as 32.
