Joint Mobility and Routing Optimization in UAV-assisted Networks via RSSI-Based Localization and Multi-Agent DRL

Unmanned aerial vehicle (UAV)-assisted communication networks offer an effective solution for establishing connectivity and enabling data delivery in scenarios where direct communication with terrestrial infrastructure is infeasible, such as in disaster areas. In such networks, unknown user locations, dynamic node mobility, and multi-hop transmission collectively increase the complexity of the communication process. Specifically, the uncertainty of user positions makes it difficult for UAVs to establish and maintain reliable communication links, while the mobility of both mobile users (MUs) and UAVs leads to frequent fluctuations in link quality. Furthermore, the dynamic network topology increases the complexity of routing in multi-hop transmissions, requiring UAVs to continuously adjust their trajectories to ensure stable and efficient data relaying. In this paper, we apply received signal strength indicator (RSSI)-based multilateration and deep reinforcement learning (DRL) to address the associated difficulties. Specifically, UAVs estimate the positions of nearby MUs using RSSI-based multilateration. The joint problem of mobility control and data forwarding is formulated as an Markov decision process (MDP), and a multi-agent proximal policy optimization (MAPPO) algorithm is employed to learn decentralized decision-making policies. Extensive simulations demonstrate that the proposed scheme improves transmission completion ratio and reduces transmission delay in UAV-assisted communication networks.