Intelligent Wireless Resource Allocation Algorithm for Unmanned Aerial Vehicle Integrated Communication and Sensing Networks in Railway Emergency Scenarios

YAN Li; YUE Tao; FANG Xuming

doi:10.11999/JEIT240254

Volume 46 Issue 9

Sep. 2024

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YAN Li, YUE Tao, FANG Xuming. Intelligent Wireless Resource Allocation Algorithm for Unmanned Aerial Vehicle Integrated Communication and Sensing Networks in Railway Emergency Scenarios[J]. Journal of Electronics & Information Technology, 2024, 46(9): 3510-3519. doi: 10.11999/JEIT240254

Citation:

YAN Li, YUE Tao, FANG Xuming. Intelligent Wireless Resource Allocation Algorithm for Unmanned Aerial Vehicle Integrated Communication and Sensing Networks in Railway Emergency Scenarios[J]. Journal of Electronics & Information Technology, 2024, 46(9): 3510-3519. doi: 10.11999/JEIT240254

Citation:

YAN Li, YUE Tao, FANG Xuming. Intelligent Wireless Resource Allocation Algorithm for Unmanned Aerial Vehicle Integrated Communication and Sensing Networks in Railway Emergency Scenarios[J]. Journal of Electronics & Information Technology, 2024, 46(9): 3510-3519. doi: 10.11999/JEIT240254

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Intelligent Wireless Resource Allocation Algorithm for Unmanned Aerial Vehicle Integrated Communication and Sensing Networks in Railway Emergency Scenarios

doi: 10.11999/JEIT240254

Sichuan Province Key Laboratory of Information Coding and Transmission, Southwest Jiaotong University, Chengdu 610031, China

Funds: The National Natural Science Foundation of China (62101460, 62071393, U2268201)

Received Date: 2024-04-09
Rev Recd Date: 2024-08-25

Available Online: 2024-08-30

Publish Date: 2024-09-26

Abstract

Abstract

In railway emergency scenarios with ground infrastructure vulnerable to damage from harsh natural environments, an Unmanned Aerial Vehicle (UAV) integrated communication and sensing wireless access network architecture is proposed in this paper, enabling real-time environmental sensing and information backhaul. Given the limited endurance of UAVs, a train braking distance model and a UAV energy consumption model are established, which are then jointly utilized to adjust the UAV flight speed and communication transmit power, optimizing overall UAV energy consumption while satisfying communication performance requirements during information backhaul and environmental sensing. Analysis reveals that this optimization problem conforms to the Markov Decision Process (MDP). Consequently, an intelligent wireless resource allocation algorithm for UAV integrated communication and sensing, grounded in the Double Deep Q Network (DDQN), is proposed to tackle the problem. The simulation results demonstrate that the proposed algorithm exhibits excellent convergence performance and maximizes the operational duration of UAV communications, while meeting the requirements for environmental sensing and information backhaul in railway emergency scenarios.

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References(22)

References

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