Work

Multiuser Detection With Compressive Sensing Iterative Reweighed Approach for Grant-Free MIMO-NOMA Systems

Year: 2024

Type: article

Abstract: In this paper, we focus on the efficient multiuser detection (MUD) problem for multiple-input multiple-output (MIMO) enabled grant-free non-orthogonal multiple access (GF-NOMA) system for massive machine-type communications (MTC). The inherent sparsity of mMTC motivates us to make use of compressive sensing (CS) technology to address the MUD problem. This paper discusses the use of an iterative reweighed (IR) scheme combined with the majorization-minimization (MM) algorithm to recover sparse signals under the Grant-free MIMO-NOMA model. Numerical and simulation results demonstrate that Grant-Free MIMO-NOMA with the proposed IR scheme is capable of reconstructing sparse signals with unknown user activity factors and the proposed algorithm outperforms conventional multiuser detectors.

Source: IEEE Transactions on Vehicular Technology

DOI: 10.1109/tvt.2024.3455188

Authors: Xiaoxu Zhang, Ziyang Zhou, Li Zhang, Pingzhi Fan, Zheng Ma

Institutions: Southwest Jiaotong University, University of Leeds

Language: English

Topic: Sparse and Compressive Sensing Techniques

Subfield: Computational Mechanics

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funder: National Natural Science Foundation of China

Awards: G1397133892, G1481524095, G6018493712, G6817301719