Joint Optimization of User Association and Power Allocation in Wireless Networks Using a Large Spatio-Temporal Graph Transformer Model

In this era, Wireless Communication Networks (WCNs) need dynamic and adaptive resource allocation approaches to handle user association and power allocation specifically under multi-connectivity and diverse traffic conditions. However, the conventional approaches struggle due to high computational cost, poor adaptability, and limited generalization. Therefore, this research proposes a large Spatio-Temporal Graph Transformer-based Reinforcement Learning (STGT-RL) model to jointly optimize user association and power allocation in large-scale WCNs. Initially, the network topology is designed using graph representations and incorporates a hybrid encoder that integrates Graph Transformers for spatial user-Base Station (BS) relationships and Spatio-Temporal Transformers for capturing time-varying traffic and channel states. Further, to ensure adaptive decision-making, a Transformer-RL policy agent is trained through a multi-objective reward function that assists in balancing throughput maximization and power efficiency. Furthermore, to enable stable policy learning, the model is initially trained using high-quality supervision from CRFSMA-generated labels, followed by reinforcement-based policy refinement. Hence, the experimental results are simulated on WCN environments to demonstrate that the proposed STGT-RL significantly outperforms baseline deep learning and heuristic-based methods in terms of throughput, energy efficiency, and fairness.