Greening 5G : Empowering Dynamic DRX with Deep Reinforcement Learning and O-RAN

Dynamic Discontinuous Reception (D-DRX) is an innovative power-saving mechanism for mobile devices in cellular networks, enhancing traditional DRX by enabling real-time adjustments to DRX parameters based on network conditions and dynamic traffic patterns. In this demonstration, we showcase a novel implementation of D-DRX on top of the open-source OpenAirInterface (OAI) [1] 5G platform, namely Deep Reinforcement Learning based Energy Saver (DRL-ES). DRL-ES is running as an xApp within the open-source O-RAN compliant RIC, FlexRIC [2], allowing for seamless adaptation to changing network dynamics. The DRL-ES operates by continuously monitoring Radio Link Control (RLC) latency data as input from the Distributed Unit (DU). DRL-ES leverages the Deep Q-Network (DQN) algorithm, which is trained to select the best DRX parameters, specifically the on-duration parameter for each User Equipment (UE). This focused approach optimizes the UE's active listening period within each DRX cycle based on observed RLC latency and achieves a balance between energy saving and Low latency. The DRL-ES utilizes two O-RAN compliant Service Models (SM): the Key Performance Measurement (KPM) SM to obtain RLC latency input and the RAN Control (RC) SM to send the on-duration values. This implementation adheres to existing 3GPP and O-RAN standards while demonstrating the potential of Artificial Intelligence (AI) driven optimization in 5G networks and beyond. Using DRL-ES, network operators can enhance battery life for mobile devices without compromising performance, particularly critical services.