Mixture-of-Experts Deep Reinforcement Learning for Reliability-Constrained Energy-Efficient PDCCH Monitoring in Internet of Thing Device

Yue Xiu, Ning Wei, Zixian Song, Tianyu Liu

The continuous monitoring of the physical downlink control channel (PDCCH) is a major source of energy consumption in fifth-generation (5G) Internet of thing device (IoT-D), since the UE has to blindly detect downlink control information even when no valid scheduling grant is present. Although predictive dynamic power management can reduce unnecessary receiver activity by skipping PDCCH monitoring in grant-free slots, aggressive sleeping may lead to missed grants and degrade reception reliability. To address this tradeoff, this paper formulates UE-side PDCCH monitoring as a reliability-constrained long-term energy minimization problem. Specifically, the IoT-D determines, before observing the actual scheduling outcome, whether to monitor the PDCCH or switch the receiver chain into a low-power state. The objective is to minimize the long-term average energy consumption, including receiver operating energy, component switching energy, and prediction-related computational energy, while ensuring that the false negative rate of scheduling-grant detection remains below a prescribed threshold. The resulting problem is non-convex due to the bursty and temporally correlated nature of grant arrivals, and the binary monitoring decisions coupled by a long-term reliability constraint. To solve this problem, we propose a mixture-of-experts input-output hidden Markov model (MoE-IOHMM)-based predictive monitoring scheme, where multiple IO-HMM experts capture heterogeneous grant-arrival patterns and a gating network adaptively combines their predictions. Simulation results show that the proposed scheme effectively reduces IoT-D-side energy consumption compared with always-on PDCCH monitoring and conventional predictive baselines, while maintaining the false negative rate below the prescribed reliability threshold.