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

Tian-Tian Lin, Yi Liu, Xiao-Wei Tang, Yunmei Shi, Yi Huang, Zhongxiang Wei, Qingqing Wu, Yuhan Dong

TL;DR

This work tackles 3D trajectory planning for UAV-assisted VLC data collection by deriving a closed-form optimal flight altitude under a VLC channel-gain threshold and solving the horizontal path planning with an improved TD3 algorithm enhanced by a pheromone-driven reward. The method jointly optimizes altitude and horizontal trajectory while accounting for Lambertian VLC channel characteristics, user coverage constraints, and area/safe-flight restrictions. Key contributions include a theoretical altitude expression that balances communication reliability and flight efficiency, and a novel reward mechanism that accelerates TD3 convergence without sacrificing stability. The results demonstrate meaningful flight-distance reductions and faster convergence, highlighting practical gains for UAV-enabled VLC networks in dynamic environments.

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.

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

TL;DR

This work tackles 3D trajectory planning for UAV-assisted VLC data collection by deriving a closed-form optimal flight altitude under a VLC channel-gain threshold and solving the horizontal path planning with an improved TD3 algorithm enhanced by a pheromone-driven reward. The method jointly optimizes altitude and horizontal trajectory while accounting for Lambertian VLC channel characteristics, user coverage constraints, and area/safe-flight restrictions. Key contributions include a theoretical altitude expression that balances communication reliability and flight efficiency, and a novel reward mechanism that accelerates TD3 convergence without sacrificing stability. The results demonstrate meaningful flight-distance reductions and faster convergence, highlighting practical gains for UAV-enabled VLC networks in dynamic environments.

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.
Paper Structure (10 sections, 12 equations, 3 figures, 1 algorithm)

This paper contains 10 sections, 12 equations, 3 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model and Problem Formulation
  3. System Model
  4. Problem Formulation
  5. Proposed Solution
  6. TD3-based Trajectory Planning Algorithm
  7. Derivation of the Optimal UAV Flight Altitude
  8. Design of the Pheromone-Driven Reward Mechanism
  9. Numerical Results
  10. Conclusions

Figures (3)

  • Figure 1: UAV-assisted VLC communication system.
  • Figure 2: Numerical results: (a) relationship between the UAV's flight distance and its altitude, (b) the UAV's horizontal trajectory, and (c) performance of the convergence acceleration.
  • Figure 3: Comparison of total flight distance with different algorithms.