Joint Trajectory Control, Frequency Allocation, and Routing for UAV Swarm Networks: A Multi-Agent Deep Reinforcement Learning Approach

Alam, Muhammad Morshed; Moh, Sangman

doi:10.1109/tmc.2024.3403890

IEEE Trans. on Mobile Comput.

2024

DOI: 10.1109/tmc.2024.3403890

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Joint Trajectory Control, Frequency Allocation, and Routing for UAV Swarm Networks: A Multi-Agent Deep Reinforcement Learning Approach

Muhammad Morshed Alam,

Sangman Moh

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Cited by 2 publications

References 39 publications

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Energy-Saving Multi-Agent Deep Reinforcement Learning Algorithm for Drone Routing Problem

Shu,

Lin,

Wen

2024

Sensors

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With the rapid advancement of drone technology, the efficient distribution of drones has garnered significant attention. Central to this discourse is the energy consumption of drones, a critical metric for assessing energy-efficient distribution strategies. Accordingly, this study delves into the energy consumption factors affecting drone distribution. A primary challenge in drone distribution lies in devising optimal, energy-efficient routes for drones. However, traditional routing algorithms, predominantly heuristic-based, exhibit certain limitations. These algorithms often rely on heuristic rules and expert knowledge, which can constrain their ability to escape local optima. Motivated by these shortcomings, we propose a novel multi-agent deep reinforcement learning algorithm that integrates a drone energy consumption model, namely EMADRL. The EMADRL algorithm first formulates the drone routing problem within a multi-agent reinforcement learning framework. It subsequently designs a strategy network model comprising multiple agent networks, tailored to address the node adjacency and masking complexities typical of multi-depot vehicle routing problem. Training utilizes strategy gradient algorithms and attention mechanisms. Furthermore, local and sampling search strategies are introduced to enhance solution quality. Extensive experimentation demonstrates that EMADRL consistently achieves high-quality solutions swiftly. A comparative analysis against contemporary algorithms reveals EMADRL’s superior energy efficiency, with average energy savings of 5.96% and maximum savings reaching 12.45%. Thus, this approach offers a promising new avenue for optimizing energy consumption in last-mile distribution scenarios.

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Energy-Saving Multi-Agent Deep Reinforcement Learning Algorithm for Drone Routing Problem

Shu,

Lin,

Wen

2024

Sensors

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Cluster channel equalization using adaptive sensing and reinforcement learning for UAV communication

Liu,

Zhao,

Liang

et al. 2024

PeerJ Computer Science

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Aiming to address the need for dynamic sensing and channel equalization in UAV cluster communication environments, this article introduces an equalization algorithm based on a U-Net model and fuzzy reinforcement Q-learning (U-FRQL-EA). This algorithm is designed to enhance the channel sensing and equalization capabilities of UAV communication systems. Initially, we develop a U-Net-based signal processing algorithm that effectively reduces acoustic noise in UAV communication channels and enables real-time, accurate perception of channel states by automatically learning channel features. Subsequently, we enhance fuzzy reinforcement Q-learning by incorporating a fuzzy neural network to approximate the Q-values and integrating this approach with the allocation strategy of wireless sensing nodes. This enhancement not only improves the accuracy of Q-value approximation but also increases the algorithm’s adaptability and decision-making ability in complex channel environments. Finally, we construct the U-FRQL-EA equalization algorithm by combining the improved U-Net model with fuzzy reinforcement Q-learning. This algorithm leverages the U-Net model to sense channel states in real time and intelligently adjusts data forwarding strategies based on fuzzy values generated by the fuzzy reinforcement Q-learning. Simulation results demonstrate that the U-FRQL-EA algorithm effectively reduces the system’s bit error rate, enhances communication quality, and optimizes network resource utilization, offering a novel solution for improving the performance of uncrewed aerial vehicle communication systems.

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Joint Trajectory Control, Frequency Allocation, and Routing for UAV Swarm Networks: A Multi-Agent Deep Reinforcement Learning Approach

Cited by 2 publications

References 39 publications

Energy-Saving Multi-Agent Deep Reinforcement Learning Algorithm for Drone Routing Problem

Energy-Saving Multi-Agent Deep Reinforcement Learning Algorithm for Drone Routing Problem

Cluster channel equalization using adaptive sensing and reinforcement learning for UAV communication

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