Deep Reinforcement Learning for Unmanned Aerial Vehicle-Assisted Vehicular Networks

TL;DR

This paper applies deep reinforcement learning, specifically DDPG, to optimize UAV-assisted vehicular networks by jointly controlling UAV flight and communication to maximize throughput and energy efficiency.

Contribution

It introduces a novel DDPG-based approach for joint UAV control and communication optimization in vehicular networks, considering energy constraints and mobility prediction.

Findings

Proposed algorithms outperform baseline schemes in throughput.

Modified DDPG framework adapts to UAV mobility and energy considerations.

Verification confirms near-optimal performance in simplified models.

Abstract

Unmanned aerial vehicles (UAVs) are envisioned to complement the 5G communication infrastructure in future smart cities. Hot spots easily appear in road intersections, where effective communication among vehicles is challenging. UAVs may serve as relays with the advantages of low price, easy deployment, line-of-sight links, and flexible mobility. In this paper, we study a UAV-assisted vehicular network where the UAV jointly adjusts its transmission control (power and channel) and 3D flight to maximize the total throughput. First, we formulate a Markov decision process (MDP) problem by modeling the mobility of the UAV/vehicles and the state transitions. Secondly, we solve the target problem using a deep reinforcement learning method, namely, the deep deterministic policy gradient (DDPG), and propose three solutions with different control objectives. Deep reinforcement learning methods obtain the optimal policy through the interactions with the environment without knowing the environment variables. Considering that environment variables in our problem are unknown and unmeasurable, we choose a deep reinforcement learning method to solve it. Moreover, considering the energy consumption of 3D flight, we extend the proposed solutions to maximize the total throughput per unit energy. To encourage or discourage the UAV's mobility according to its prediction, the DDPG framework is modified, where the UAV adjusts its learning rate automatically. Thirdly, in a simplified model with small state space and action space, we verify the optimality of proposed algorithms. Comparing with two baseline schemes, we demonstrate the effectiveness of proposed algorithms in a realistic model.