Sparse Detection of Non-Sparse Signals for Large-Scale Wireless Systems

TL;DR

This paper presents a novel detection algorithm for large-scale wireless systems that enhances traditional linear detection by employing sparse error recovery, resulting in significant performance improvements with minimal additional computational cost.

Contribution

The paper introduces the PSED algorithm, combining sparse transformation and error recovery to improve detection accuracy in large-scale wireless systems.

Findings

PSED significantly outperforms classical linear detectors in large-scale systems.

The algorithm achieves these gains with low computational overhead.

Empirical results validate the theoretical analysis of MSE improvements.

Abstract

In this paper, we introduce a new detection algorithm for large-scale wireless systems, referred to as post sparse error detection (PSED) algorithm, that employs a sparse error recovery algorithm to refine the estimate of a symbol vector obtained by the conventional linear detector. The PSED algorithm operates in two steps: 1) sparse transformation converting the original non-sparse system into the sparse system whose input is an error vector caused by the symbol slicing and 2) estimation of the error vector using the sparse recovery algorithm. From the asymptotic mean square error (MSE) analysis and empirical simulations performed on large-scale systems, we show that the PSED algorithm brings significant performance gain over classical linear detectors while imposing relatively small computational overhead.