Joint User Activity and Data Detection in Grant-Free NOMA using Generative Neural Networks

TL;DR

This paper introduces a generative neural network-based multi-user detection framework for grant-free NOMA systems, achieving high accuracy with low complexity and robustness across various conditions.

Contribution

It proposes a novel low-complexity neural network architecture for user activity detection in grant-free NOMA, improving accuracy and efficiency over existing methods.

Findings

Significant improvements in detection accuracy over traditional methods.

High robustness of the sparsity estimator to channel variations.

Low computational cost due to small fixed-step reconstruction.

Abstract

Grant-free non-orthogonal multiple access (NOMA) is considered as one of the supporting technology for massive connectivity for future networks. In the grant-free NOMA systems with a massive number of users, user activity detection is of great importance. Existing multi-user detection (MUD) techniques rely on complicated update steps which may cause latency in signal detection. In this paper, we propose a generative neural network-based MUD (GenMUD) framework to utilize low-complexity neural networks, which are trained to reconstruct signals in a small fixed number of steps. By exploiting the uncorrelated user behaviours, we design a network architecture to achieve higher recovery accuracy with a low computational cost. Experimental results show significant performance gains in detection accuracy compared to conventional solutions under different channel conditions and user sparsity levels. We also provide a sparsity estimator through extensive experiments. Simulation results of the sparsity estimator showed high estimation accuracy, strong robustness to channel variations and neglectable impact on support detection accuracy.