Scheduling and resource allocation in 5G SCMA cellular networks using AI-based methods

The deployment of 5G networks has incorporated advanced multiple access technologies like sparse code multiple access (SCMA) to address growing demands for high-speed connectivity and massive device access. As a Non-Orthogonal Multiple Access technique, SCMA enables multiple users to share identical time-frequency resources through sparse codebook-based multiplexing. Nevertheless, achieving efficient scheduling in SCMA networks remains challenging due to the inherent complexities in dynamic resource allocation. This paper proposed two artificial intelligence-based approaches for resource scheduling in 5G SCMA networks: a multi-agent deep reinforcement learning (MARL)-based approach and a large language model (LLM)-empowered methodology. We systematically investigate these AI techniques to develop adaptive resource scheduling policies capable of responding to diverse network conditions. Simulation results validate that the proposed MARL-based and LLM-based schedulers not only effectively learn optimal scheduling strategies but also outperform conventional algorithms, particularly in terms of system throughput and user fairness metrics.