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

• URL: http://arxiv.org/abs/2307.13158v2
• Date: Mon, 22 Jan 2024 03:47:17 GMT
• Title: Multi-UAV Speed Control with Collision Avoidance and Handover-aware Cell Association: DRL with Action Branching
• Abstract summary: This paper presents a solution for optimizing multi-UAV cell-association decisions and their moving velocity on a 3D aerial highway. 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. Simulation results show a significant improvement of 18.32% compared to existing benchmarks.
• Score: 13.84737967579164
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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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