Decoding by Sampling - Part II: Derandomization and Soft-output Decoding

TL;DR

This paper introduces a derandomized sampling decoding algorithm that improves performance and reduces complexity, enabling near-ML and near-MAP decoding in lattice and MIMO systems through deterministic sampling.

Contribution

The paper presents a novel derandomized sampling algorithm with performance bounds and demonstrates its effectiveness for lattice decoding and soft-output decoding in MIMO systems.

Findings

Achieves near-ML performance with moderate sample size K.

Provides an upper bound on sphere radius R for list sphere decoding.

Demonstrates near-MAP performance in MIMO systems.

Abstract

In this paper, a derandomized algorithm for sampling decoding is proposed to achieve near-optimal performance in lattice decoding. By setting a probability threshold to sample candidates, the whole sampling procedure becomes deterministic, which brings considerable performance improvement and complexity reduction over to the randomized sampling. Moreover, the upper bound on the sample size K, which corresponds to near-maximum likelihood (ML) performance, is derived. We also find that the proposed algorithm can be used as an efficient tool to implement soft-output decoding in multiple-input multiple-output (MIMO) systems. An upper bound of the sphere radius R in list sphere decoding (LSD) is derived. Based on it, we demonstrate that the derandomized sampling algorithm is capable of achieving near-maximum a posteriori (MAP) performance. Simulation results show that near-optimum performance can be achieved by a moderate size K in both lattice decoding and soft-output decoding.