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

• URL: http://arxiv.org/abs/2402.08022v1
• Date: Mon, 12 Feb 2024 19:39:07 GMT
• Title: Leveraging Digital Cousins for Ensemble Q-Learning in Large-Scale Wireless Networks
• Authors: Talha Bozkus, Urbashi Mitra
• Abstract summary: A novel ensemble Q-learning algorithm is presented to optimize wireless networks. The proposed algorithm can achieve up to 50% less average error with up to 40% less runtime complexity than the state-of-the-art reinforcement learning algorithms.
• Score: 21.30645601474163
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 an extension of the traditional digital twin concept wherein multiple Q-learning algorithms on multiple synthetic Markovian environments are run in parallel and their outputs are fused into a single Q-function. Convergence analyses of key statistics and Q-functions and derivations of upper bounds on the estimation bias and variance are provided. Numerical results across a variety of real-world wireless networks show that the proposed algorithm can achieve up to 50% less average policy error with up to 40% less runtime complexity than the state-of-the-art reinforcement learning algorithms. It is also shown that theoretical results properly predict trends in the experimental results.

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