On using Deep Reinforcement Learning to balance Power Consumption and Latency in 5G NR

Future generation cellular networks consider Power Consumption (PC) as a key concern in designing and operating wireless communication systems. In this context, 3GPP has proposed several techniques to reduce User Equipment (UE) PC, such as Connected-mode Discontinuous Reception (C-DRX), with a new set of parameters introduced by 5G New Radio (NR) and BandWidth Part (BWP) adaptation. However, they did not specify how to derive the C-DRX parameters and BWP configuration that reduce the PC while avoiding latency overflow. To address this shortcoming, we propose a novel solution to jointly derive the C-DRX parameters and the BWP configuration to find a trade-off between low PC and low latency. Given the inherent dynamics and uncertainty in wireless network environments, our solution relies on Deep Reinforcement Learning (DRL) to learn from the dynamic traffic pattern and derive the best C-DRX and BWP configuration that minimizes PC while achieving low latency. Simulation results demonstrate the effectiveness of the proposed methodology in reducing the PC (i.e., 50-95% power gain) while avoiding latency overflow for a different number of connected UEs (i.e., 1 to 20 UEs).