Serve Yourself! Federated Power Control for AI-Native 5G and Beyond

Abstract

The adoption of the Industrial Internet of Things (IIoT) in industries necessitates advancements in energy efficiency and latency reduction, especially for resource-constrained devices. Services require specific Quality of Service (QoS) levels to function properly, and meeting a threshold QoS can be sufficient for smooth connectivity, reducing the need to maximize perceived QoS due to energy concerns. This is modeled as a satisfactory game, aiming to find minimal power allocation to meet target demands. Due to environmental uncertainties, achieving a Robust Satisfactory Equilibrium (RSE) can be challenging, leading to less satisfaction. We propose a fully distributed, environment-aware power control scheme to enhance satisfaction in dynamic environments. The proposed Robust Banach-Picard (RBP) learning scheme combines deep learning and federated learning to overcome channel and interference impacts and accelerate convergence. Extensive simulations evaluate the scheme under varying channel states and QoS demands, with discussions on convergence speed, energy efficiency, scalability, complexity, and violation rate.