Generalization Performance of Deep Learning-based Multi-User Detection for GF-SCMA Systems

Deep learning (DL) has emerged as a transformative tool in wireless communications, offering powerful methods for optimizing complex systems. One critical challenge in deploying DL-based solutions is ensuring that models can generalize effectively across various channel conditions. This paper investigates the generalization capabilities of a DL-based Collision-Aware Multi-User Detection (CA-MUD) system within Grant-Free Sparse Code Multiple Access (GF-SCMA) frameworks, which are vital for efficient massive machine-type communication (mMTC). By exploring different training environments, such as independent and identically distributed (IID) Rayleigh and tapped delay line (TDL) channels, the study aims to identify optimal strategies to maintain robust communication performance under diverse conditions.