DRL-Enabled Trajectory Planing for UAV-Assisted VLC: Optimal Altitude and Reward Design

TL;DR

This paper proposes a deep reinforcement learning framework for optimizing UAV trajectories in VLC systems, focusing on altitude and reward design to improve data collection efficiency and reduce flight distance.

Contribution

It introduces a novel pheromone-driven reward mechanism combined with deep RL for adaptive UAV trajectory planning in VLC, including a closed-form optimal altitude derivation.

Findings

Optimal altitude reduces flight distance by up to 35%.

Reward mechanism shortens convergence steps by approximately 50%.

Framework enhances data collection efficiency in UAV-assisted VLC.

Abstract

Recently, the integration of unmanned aerial vehicle (UAV) and visible light communication (VLC) technologies has emerged as a promising solution to offer flexible communication and efficient lighting. This letter investigates the three-dimensional trajectory planning in a UAV-assisted VLC system, where a UAV is dispatched to collect data from ground users (GUs). The core objective is to develop a trajectory planning framework that minimizes UAV flight distance, which is equivalent to maximizing the data collection efficiency. This issue is formulated as a challenging mixed-integer non-convex optimization problem. To tackle it, we first derive a closed-form optimal flight altitude under specific VLC channel gain threshold. Subsequently, we optimize the UAV horizontal trajectory by integrating a novel pheromone-driven reward mechanism with the twin delayed deep deterministic policy gradient algorithm, which enables adaptive UAV motion strategy in complex environments. Simulation results validate that the derived optimal altitude effectively reduces the flight distance by up to 35% compared to baseline methods. Additionally, the proposed reward mechanism significantly shortens the convergence steps by approximately 50%, demonstrating notable efficiency gains in the context of UAV-assisted VLC data collection.