Adaptive and Energy-Efficient Deployment of Robotic Airborne Base Stations: A Deep Reinforcement Learning Approach

The increasing energy demands of future wireless networks drive the need for intelligent and adaptive deployment strategies. Traditional methods often lack the flexibility required to handle the spatio-temporal fluctuations inherent in modern communication environments. To address this challenge, we investigate the energy-efficient deployment of Robotic Airborne Base Stations (RABSs) in practical scenarios, such as managing sudden traffic surges during large-scale public events and providing emergency coverage in disaster-stricken areas where terrestrial infrastructure is compromised. We propose a novel Deep Reinforcement Learning (DRL)-based framework for an energy-efficient deployment of multiple RABSs. Unlike existing approaches, our framework features both centralized and decentralized Actor-Critic DRL, enabling scalable and adaptive decision-making. The centralized model leverages global network information to optimize the collective deployment of RABSs, while the multi-agent decentralized approach allows RABSs to make independent yet coordinated decisions based on local observations, ensuring scalability in large-scale networks. In addition, we introduce a state-action representation that captures spatio-temporal traffic variations and energy consumption dynamics. Our simulations validate the effectiveness of the proposed framework, demonstrating significant improvements in energy efficiency and adaptability compared to heuristic, Gauss-Markov, and Q-Learning models. Furthermore, comparison with an exhaustive search benchmark confirms that our approach achieves an optimal energy efficiency with significantly lower computational complexity.