Digital-Twin-Enabled Channel Access and Power Control for Smart Grids in Communication Networks

In this paper, we propose a novel channel access and power control scheme for smart grids in communication networks. The scheme is named digital twin-based memory recall optimization (DMRO), which aims to extract meaningful patterns from noisy network traffic measurements and support more sophisticated decision-making processes for optimizing channel access and power control for smart grids. Specifically, we design a pattern extraction method that minimizes the Frobnius norm between the collected measurements and the expected k-rank approximation of the measurements in order to extract useful information. Then, considering the interference and signal-to-interference-plus-noise ratio (SINR) constraints in the wireless environment, we develop a digital twin-based distributed channel access and power control scheme to improve the latency taming and energy utilization efficiency of the phasor measurements units (PMU). We consider both the real-time traffic prediction and the paired optimization scheme on the digital twin side, and utilize memory recall to enhance local model robustness to optimize from a more diverse set of situations by replaying underrepresented experiences. Simulation results demonstrate that the proposed DMRO scheme can achieve high traffic prediction accuracy and improve the latency taming and energy utilization efficiency even increasing industrial channel interference or the number of PMUs.