UAV Trajectory Optimization for Directional THz Links Using Deep Reinforcement Learning

TL;DR

This paper proposes a deep reinforcement learning approach to optimize UAV trajectories in a 3D dynamic HetNet with directional THz links, aiming to minimize outages for disaster recovery scenarios.

Contribution

It introduces a novel DRL-based algorithm for UAV trajectory optimization in 3D THz-enabled HetNets, addressing a critical challenge in disaster recovery communications.

Findings

DRL algorithm effectively minimizes outage probability.

Simulation results show improved link reliability.

Proposed method adapts to dynamic network conditions.

Abstract

As an alternative solution for quick disaster recovery of backhaul/fronthaul links, in this paper, a dynamic unmanned aerial vehicles (UAV)-assisted heterogeneous (HetNet) network equipped with directional terahertz (THz) antennas is studied to solve the problem of transferring traffic of distributed small cells. To this end, we first characterize a detailed three-dimensional modeling of the dynamic UAV-assisted HetNet, and then, we formulate the problem for UAV trajectory to minimize the maximum outage probability of directional THz links. Then, using deep reinforcement learning (DRL) method, we propose an efficient algorithm to learn the optimal trajectory. Finally, using simulations, we investigate the performance of the proposed DRL-based trajectory method.