Novel Learning-Based Multi-User Detection Algorithms for Spatially Correlated MTC

Emerging massive machine-type communications service class needs to support many devices while ensuring that scarce radio resources are utilized efficiently. Non-orthogonal multiple access is proposed to minimize the signaling overhead and optimize resource allocation. However, during the initial access, the base station 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 base station. In this context, we formulate a novel multi-user detection (MUD) problem in spatially correlated Rician channels, which we reformulate as a multi-label classification problem utilizing deep learning techniques. We propose two diverse approaches to tackle this problem: ViT-Net, a vision transformer-based architecture, and 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 non-correlated scenario. Numerical evaluations demonstrate the effectiveness of the proposed model in addressing spatial correlation compared to the existing deep learning models.