An unmanned aerial vehicle optimal resource allocation method based on communication control integration

By optimizing subcarrier resource allocation and control data rate in UAV swarms through system modeling and genetic algorithms, the problem of uneven resource allocation in UAV swarms was solved, and the control performance and coordination capabilities of UAV swarms were improved.

CN117076056BActive Publication Date: 2026-06-26UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
UNIV OF ELECTRONICS SCI & TECH OF CHINA
Filing Date
2023-09-27
Publication Date
2026-06-26

AI Technical Summary

Technical Problem

Existing technologies have failed to effectively coordinate the impact of control block length and control data rate on the quantization noise of control signal transmission in UAV swarm formation control, resulting in uneven competition for communication resources and affecting control performance.

Method used

By modeling the equipment, communication, and control systems, and combining genetic algorithms to optimize the subcarrier resource allocation and control data rate of the UAV swarm, an optimization problem is established to minimize the control mean square error, ensuring optimal control performance of the UAV swarm under limited resources.

Benefits of technology

It achieves optimal resource allocation in the drone swarm, ensuring fairness and efficiency in control performance, and improving the coordination and autonomy of the drone swarm.

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Abstract

The application relates to a UAV optimal resource allocation method based on communication control integration, and belongs to the technical field of communication. m (m=m, …, M), the flight state of the UAV cluster is changed through the head machine, and then the rest of the UAVs are remotely controlled through an ICAC system; device system modeling, communication system modeling and control system modeling are sequentially performed, and UAV cluster control performance analysis is performed; multi-UAV subcarrier resource allocation and control data rate optimization are performed. The application considers the influence of the control signal block length and the control data rate on the control signal quantization noise, and in addition, in the UAV cluster with limited subcarrier resources, optimal subcarrier and control data rate allocation is obtained to ensure that the control performance of all UAVs is best under fairness.
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