Multiagent Reinforcement Learning for Offloading Cellular Communications With Cooperating UAVs

Effective solutions for intelligent data collection in terrestrial cellular networks are crucial, especially in the context of Internet of Things applications. The limited spectrum and coverage area of terrestrial base stations (BSs) pose challenges in meeting the escalating data rate demands of network users. Uncrewedaerial vehicles (UAVs), known for their high agility, mobility, and flexibility, present an alternative means to offload data traffic from terrestrial BSs, serving as additional access points. In this article, we aim to efficiently maximize the utilization of multiple UAVs for data traffic offloading from terrestrial BSs. Specifically, the focus is on maximizing user association with UAVs by jointly optimizing UAV trajectories and users' association indicators under quality-of-service (QoS) constraints. Since the formulated practical UAV control problem is nonconvex and combinatorial, this study leverages the multiagent reinforcement learning (MARL) framework. Employing MARL, each UAV acts as an independent agent, aiming to maintain inter-UAV cooperative behavior. The proposed approach utilizes the finite-state Markov decision process to account for UAVs' velocity constraints and the relationship between their trajectories and state space. A low-complexity distributed state-action-reward-state-action algorithm is presented to determine UAVs' optimal sequential decision-making policies over training episodes. The extensive simulation results validate the proposed analysis and offer valuable insights into the optimal UAV trajectories. The derived trajectories demonstrate superior average UAV association performance compared to benchmark techniques, such as Q-learning and particle swarm optimization.