Reduced-dimension multiuser detection: detectors and performance guarantees

TL;DR

This paper introduces reduced-dimension multiuser detection methods that lower hardware complexity by using fewer correlators, leveraging compressed sensing ideas, and providing theoretical performance guarantees validated through simulations.

Contribution

The paper develops a general framework for RD-MUD, introduces two novel detectors, and provides theoretical analysis with validation, advancing multiuser detection efficiency.

Findings

RD-MUD achieves similar performance to traditional methods with fewer correlators.

The linear RDD detector effectively identifies active users and data signs.

The nonlinear RDDF detector improves detection accuracy through decision-feedback techniques.

Abstract

We explore several reduced-dimension multiuser detection (RD-MUD) structures that significantly decrease the number of required correlation branches at the receiver front-end, while still achieving performance similar to that of the conventional matched-filter (MF) bank. RD-MUD exploits the fact that the number of active users is typically small relative to the total number of users in the system and relies on ideas of analog compressed sensing to reduce the number of correlators. We first develop a general framework for both linear and nonlinear RD-MUD detectors. We then present theoretical performance analysis for two specific detectors: the linear reduced-dimension decorrelating (RDD) detector, which combines subspace projection and thresholding to determine active users and sign detection for data recovery, and the nonlinear reduced-dimension decision-feedback (RDDF) detector, which combines decision-feedback orthogonal matching pursuit for active user detection and sign detection for data recovery. The theoretical performance results for both detectors are validated via numerical simulations.