NDA-CoMBS: Network Demand-Aware Autonomous Cooperative Decision-Making for Mobile Base Stations

Dynamically deploying mobile base stations is cru-cial for meeting public network demands and optimizing utilization. Traditional dynamic deployment strategies focus on maxi-mizing coverage of Points of Interest (POls), often overlooking the dynamic network demands of these locations. To address this problem, we propose a reinforcement learning-based dynamic deployment method. More specifically, we first design a spatio- temporal modeling-based actor network to comprehensively cap-ture network demand dynamics and make decisions for the next locations. Second, we design an attention-based critic network to efficiently process information and more accurately estimate collaborative decision-making. Third, we formulate a heuristic reward function to align the objectives of maximizing cumulative service utilization and minimizing energy consumption. Finally, we develop a digital twin simulator to validate the proposed method. Experiments using real-world data demonstrate the effectiveness of our approach.