Leveraging Digital Cousins for Ensemble Q-Learning in Large-Scale Wireless Networks

Optimizing large-scale wireless networks, including optimal resource management, power allocation, and throughput maximization, is inherently challenging due to their non-observable system dynamics and heterogeneous and complex nature. Herein, a novel ensemble Q-learning algorithm that addresses the performance and complexity challenges of the traditional Q-learning algorithm for optimizing wireless networks is presented. Ensemble learning with synthetic Markov Decision Processes is tailored to wireless networks via new models for approximating large state-space observable wireless networks. In particular, digital cousins are proposed as …

algorithm challenges complexity cs.lg cs.ni digital dynamics eess.sp ensemble management nature networks novel observable performance power q-learning scale wireless