Soft-Input Soft-Output Single Tree-Search Sphere Decoding

TL;DR

This paper introduces a low-complexity SISO sphere-decoding algorithm for MIMO systems that incorporates LLR clipping and a correction method for approximate LLRs, enhancing performance and reducing computational load.

Contribution

It presents a novel single tree-search SISO sphere-decoding algorithm with LLR clipping and a correction technique for sub-optimal detectors, improving efficiency and detection accuracy.

Findings

Significant complexity reduction through LLR clipping.

Enhanced detection performance with the correction method.

Flexible performance/complexity tradeoff via a single parameter.

Abstract

Soft-input soft-output (SISO) detection algorithms form the basis for iterative decoding. The computational complexity of SISO detection often poses significant challenges for practical receiver implementations, in particular in the context of multiple-input multiple-output (MIMO) wireless communication systems. In this paper, we present a low-complexity SISO sphere-decoding algorithm, based on the single tree-search paradigm proposed originally for soft-output MIMO detection in Studer, et al., IEEE J-SAC, 2008. The new algorithm incorporates clipping of the extrinsic log-likelihood ratios (LLRs) into the tree-search, which results in significant complexity savings and allows to cover a large performance/complexity tradeoff region by adjusting a single parameter. Furthermore, we propose a new method for correcting approximate LLRs --resulting from sub-optimal detectors-- which (often significantly) improves detection performance at low additional computational complexity.