Data-aided Active User Detection with a User Activity Extraction Network for Grant-free SCMA Systems

TL;DR

This paper introduces a novel autoencoder-based approach with a user activity extraction network for improved active user detection in grant-free SCMA systems, achieving significant performance gains.

Contribution

It proposes a joint optimization framework combining preamble generation and data-aided user detection using a new UAEN architecture with self-supervised pre-training.

Findings

Achieved 3-5dB gain in detection performance.

Demonstrated effective joint optimization of contention resources.

Enhanced convergence with self-supervised pre-training.

Abstract

In grant-free sparse code multiple access (GF-SCMA) system, active user detection (AUD) is a major performance bottleneck as it involves complex combinatorial problem, which makes joint design of contention resources for users and AUD at the receiver a crucial but a challenging problem. To this end, we propose autoencoder (AE)-based joint optimization of both preamble generation networks (PGNs) in the encoder side and data-aided AUD in the decoder side. The core architecture of the proposed AE is a novel user activity extraction network (UAEN) in the decoder that extracts a priori user activity information from the SCMA codeword data for the data-aided AUD. An end-to-end training of the proposed AE enables joint optimization of the contention resources, i.e., preamble sequences, each associated with one of the codebooks, and extraction of user activity information from both preamble and SCMA-based data transmission. Furthermore, we propose a self-supervised pre-training scheme for the UAEN prior to the end-to-end training, to ensure the convergence of the UAEN which lies deep inside the AE network. Simulation results demonstrated that the proposed AUD scheme achieved 3 to 5dB gain at a target activity detection error rate of $\bf{{10}^{-3}}$ compared to the state-of-the-art DL-based AUD schemes.