Service Provisioning and Path Planning with Obstacle Avoidance for Low-Altitude Wireless Networks

TL;DR

This paper presents a comprehensive approach for deploying UAV-based aerial base stations in complex environments, optimizing their trajectories and associations to maximize user satisfaction while avoiding obstacles and handling UAV failures.

Contribution

It introduces a joint optimization framework for UAV trajectory, beamforming, and user association using deep reinforcement learning and decomposition techniques.

Findings

Outperforms baseline schemes in convergence speed.

Achieves efficient user association and obstacle avoidance.

Enhances overall system performance in complex environments.

Abstract

This paper investigates the three-dimensional (3D) deployment of uncrewed aerial vehicles (UAVs) as aerial base stations in heterogeneous communication networks under constraints imposed by diverse ground obstacles. Given the diverse data demands of user equipments (UEs), a user satisfaction model is developed to provide personalized services. In particular, when a UE is located within a ground obstacle, the UAV must approach the obstacle boundary to ensure reliable service quality. Considering constraints such as UAV failures due to battery depletion, heterogeneous UEs, and obstacles, we aim to maximize overall user satisfaction by jointly optimizing the 3D trajectories of UAVs, transmit beamforming vectors, and binary association indicators between UAVs and UEs. To address the complexity and dynamics of the problem, a block coordinate descent method is adopted to decompose it into two subproblems. The beamforming subproblem is efficiently addressed via a bisection-based water-filling algorithm. For the trajectory and association subproblem, we design a deep reinforcement learning algorithm based on proximal policy optimization to learn an adaptive control policy. Simulation results demonstrate that the proposed scheme outperforms baseline schemes in terms of convergence speed and overall system performance. Moreover, it achieves efficient association and accurate obstacle avoidance.