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arXiv · 2307.13158

Multi-UAV Speed Control with Collision Avoidance and Handover-aware Cell Association: DRL with Action Branching

Zijiang Yan·Wael Jaafar·Bassant Selim·Hina Tabassum
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Abstract

This paper presents a deep reinforcement learning solution for optimizing multi-UAV cell-association decisions and their moving velocity on a 3D aerial highway. The objective is to enhance transportation and communication performance, including collision avoidance, connectivity, and handovers. The problem is formulated as a Markov decision process (MDP) with UAVs' states defined by velocities and communication data rates. We propose a neural architecture with a shared decision module and multiple network branches, each dedicated to a specific action dimension in a 2D transportation-communication space. This design efficiently handles the multi-dimensional action space, allowing independence for individual action dimensions. We introduce two models, Branching Dueling Q-Network (BDQ) and Branching Dueling Double Deep Q-Network (Dueling DDQN), to demonstrate the approach. Simulation results show a significant improvement of 18.32% compared to existing benchmarks.

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Zijiang Yan, Wael Jaafar, Bassant Selim, Hina Tabassum. 2023-07-24. Multi-UAV Speed Control with Collision Avoidance and Handover-aware Cell Association: DRL with Action Branching. https://arxiv.org/abs/2307.13158

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