Cognitive unmanned aerial vehicle network spectrum sharing method based on multi-agent reinforcement learning

By using multi-agent reinforcement learning method to allocate real-time spectrum resources in cognitive drone networks, the problems of waste and low utilization of spectrum resources in the existing technology are solved, and efficient spectrum sharing and throughput are achieved.

CN119697646APending Publication Date: 2025-03-25XIDIAN UNIV
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Patent Information

Application Number
CN202411855995.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-17
Publication Date
2025-03-25

AI Technical Summary

Technical Problem

The existing cognitive drone network spectrum sharing method has problems of wasted spectrum resources and low spectrum utilization when facing scenarios of rapid environmental changes and diversified demands.

Method used

The spectrum sharing method of cognitive drone network based on multi-agent reinforcement learning is adopted. By constructing a cognitive drone network model and multi-agent reinforcement learning interactive network model, the reinforcement learning algorithm is used for real-time interaction and spectrum resource allocation, and the spectrum resource allocation strategy is adaptively adjusted.

Benefits of technology

It effectively reduces the waste of spectrum resources, avoids spectrum conflicts, maximizes network throughput, and thus improves spectrum utilization.

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Abstract

The invention provides a cognitive unmanned aerial vehicle network spectrum sharing method based on multi-agent reinforcement learning. The method comprises the implementation steps that a cognitive unmanned aerial vehicle network model and a cognitive unmanned aerial vehicle network and multi-agent reinforcement learning interaction network model are constructed; obtaining a training data set and defining a reward function; and carrying out iterative training on the interactive network model. In the process of training the interactive network model, the multiple agents interact with the cognitive unmanned aerial vehicle network in real time through reinforcement learning, spectrum resource allocation can be adaptively adjusted according to the communication requirement of each cognitive unmanned aerial vehicle and the dynamic change of the environment, and the spectrum resource allocation efficiency is improved. And the loss of the local Q network in each time step is calculated according to the target Q value corresponding to each experience calculated by calculating the reward of interaction between each Q agent and each environment state through the throughput of each cluster member on each channel, so that the resource waste is reduced, the spectrum conflict is avoided, and the maximization of the network throughput is realized. Therefore, the spectrum utilization rate is effectively improved.
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