Novel Learning-Based Multiuser Detection Algorithms for Spatially Correlated MTC

• Published in: IEEE internet of things journal Vol. 12; no. 13; pp. 23169 - 23181
• Main Authors: Sivalingam, Thushan, Gunarathne, Samitha, Mahmood, Nurul Huda, Ali, Samad, Rajatheva, Nandana, Latva-Aho, Matti
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.07.2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 6G mobile communication; Artificial neural networks; Channels; Computer architecture; Convergence; Correlation; Deep learning; Internet of Things; Iterative methods; Machine learning; Mud; Multiple user detection; Multiuser detection; NOMA; Nonorthogonal multiple access; Resource allocation; Rician channels; sparse code multiple access (SCMA); spatial correlation; Transformers; vision-transformer
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2025.3552215

Emerging massive machine-type communications service class needs to support many devices while ensuring that scarce radio resources are utilized efficiently. Nonorthogonal multiple access is proposed to minimize the signaling overhead and optimize resource allocation. However, during the initial access, the base station (BS) is presented with the challenge of identifying sparsely active devices in the absence of knowledge about the sparsity and channel state information. The user channels in most practical scenarios have common reflection paths, making them partially correlated, which can be exploited to improve the detection performance at the BS. In this context, we formulate a novel multiuser detection (MUD) problem in spatially correlated Rician channels, which we reformulate as a multilabel classification problem utilizing deep learning techniques. We propose two diverse approaches to tackle this problem: 1) ViT-Net, a vision transformer-based architecture, and 2) FAR-Net, a fully activated deep neural network featuring residual connections. Our analysis highlights the significance of spatial correlation for MUD, which can accord around 13% higher overloading ratio compared to the noncorrelated scenario. Numerical evaluations demonstrate the effectiveness of the proposed model in addressing spatial correlation compared to the existing deep-learning models.