Enhanced NSA-DRX Mechanism for Cognitive 5G Networks Utilizing Q-Learning and Long Short Term Rewards
Xiaohui Lian, Heng Yang, Shanshan Li, Zhenyu Liu, Xue Liu, Weiran Wang · IEEE Transactions on Cognitive Communications and Networking · 2025
Managing the growing energy demands of user equipment (UE) in 5G networks is critical for sustaining prolonged device standby times. The Discontinuous Reception (DRX) mechanism plays a pivotal role in reducing power consumption, but its implementation often results in a trade-off with increased packet delays. Addressing these challenges, we propose an enhanced NSA-DRX mechanism tailored for the NSA deployment model. This mechanism integrates bothRRC_CONNECTEDandRRC_IDLEstates, modeled using an eight-state semi-Markov process to capture the stochastic and dynamic state transitions inherent in DRX operations. The proposed mechanism employs cognitive principles by integrating the Q-learning algorithm to optimize short sleep cycles dynamically. Additionally, a Long Short Term Reward (LSTR) framework is introduced, leveraging multi-scale rewards to balance power efficiency and delay. Simulations demonstrate that the Q-learning and LSTR-enhanced NSA-DRX mechanism achieves a 10% improvement in power-saving performance compared to traditional models while maintaining a balanced trade-off with delay. These results highlight the efficacy of reinforcement learning and reward-based optimization in advancing cognitive communication and networking systems for next-generation networks.