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

ABSTRACT 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.