GNN-Based Meta-Learning Approach for Adaptive Power Control in Dynamic D2D Communications

This article proposes a deep learning-based power control method for maximizing the sum rate subject to rate requirements in the interference-limited device-to-device (D2D) communications. Based on the dynamic nature of D2D communications, we consider the environment where system parameters, such as the number of devices, rate requirements, and deployment area, unpredictably change over time. To deal with the low adaptability and scalability issues of the conventional deep learning-based approaches in dynamic environments, we develop an environment-adaptive power control method by leveraging graph neural network (GNN) architecture and meta-learning approach. In the developed method, we design the node feature and state update rule for GNN by taking into account the characteristics of power optimization problem and meta-train model by treating some past environments as meta-tasks. Simulation results demonstrate that the developed method outperforms the conventional GNN-based power control methods in terms of the average sum-throughput achievement ratio and adaptation speed to test environments.