Federated Power Control for Predictive QoS in 5G and Beyond: A Proof of Concept for URLLC

Abstract

The fifth-generation (5G) mobile standard has been designed to support new use cases such as ultra-reliable and low-latency communication (URLLC). The future 6G is envisioned to support extreme URLLC with higher QoS requirements (e.g., remote surgery, autonomous driving, etc.). URLLC applications need higher QoS that require more power allocations. Consequently, QoS variance will increases, which is intolerable for URLLC. An important amount of energy can be saved through a power control scheme. In this work, we are interested in energy-aware self-organizing networks that provide satisfactory performance for URLLC. We propose a predictive QoS paradigm to enhance satisfaction and reduce power consumption under URLLC’s constraints. A predictive QoS is an intelligent paradigm that allows Mobile/IoT-device to adjust power allocation to the minimum required to achieve the target QoS. First, we model power control as a satisfactory game, where IoT-devices aim to meet their target demands instead of maximizing them. Next, we introduce a distributed satisfactory learning scheme, called Robust Banach-Picard (RBP), to allow devices to self-adjust their power allocation to maintain reliability and latency within the tolerated range of the URLLC application. The algorithm implements deep learning and a derivative concept of federated learning to account for channel variability in power control. Extensive simulations exhibit the advantages and drawbacks of the proposed scheme for URLLC applications. Results show that RBP can maintain instantaneous reliability and latency within the tolerated request at the minimum energy costs. Consequently, RBP can be safe to use for URLLC use cases compared to conventional Banach-Picard iterates.