Energy management in wireless edge networks requires joint decisions on node sleep states and processing frequency while maintaining quality of service under bursty traffic. This paper proposes a user-centric controller that combines dynamic power management (DPM), dynamic voltage and frequency scaling (DVFS), one-step demand prediction, and Double Deep Q-Learning (Double-DQN). The controller observes current and predicted load, queue state, previous configuration, and a user-derived QoS sensitivity signal, then selects one of 15 joint active-node and voltage-frequency actions. A reproducible discrete-time simulation with 20 edge nodes was evaluated over 30 independent traffic realizations. The ridge demand predictor achieved an R-squared value of 0.965 and a normalized root-mean-square error of 7.13%. The proposed controller reduced mean network power by 32.5% relative to an always-on policy and by 11.0% relative to a reactive DPM-DVFS policy. It maintained 99.98% throughput, zero delay-target violations, and a 95th-percentile queueing delay of 9.33 ms for a 20 ms target. Removing user feedback reduced power by less than 1% but increased the delay-target violation rate to 5.47%. The results demonstrate that user-aware predictive control can achieve a favorable power-QoS tradeoff, while also identifying the limits that must be addressed before deployment in a physical radio access network.
 

deep reinforcement learning, dynamic power management, dynamic voltage and frequency scaling, energy-efficient networks, user-in-the-loop control.