A method for dynamic management of multi-dimensional resources in uav heterogeneous network

Abstract

The method for dynamic management of multi-dimensional resources of a UAV heterogeneous network disclosed by the invention aims to provide a network management strategy method capable of reducing computational complexity and improving the generalization of a learning model. The invention is realized by the following technical solutions: a unified federated learning architecture is composed of a centralized convergence layer of ground base stations and a distributed UAV execution layer, and a ground base station computing platform builds a multi-agent enhanced learning model based on any UAV heterogeneous network scene and initializes the model parameters; the distributed execution layer uses the multi-agent reinforcement learning algorithm to output multi-dimensional resource management behavior, obtains the network environment's reward and state transition feedback for the agent's behavior, and uploads the model parameters to the local base station associated with it. The ground base station obtains all local model parameters through interaction and sends the updated parameters to each UAV learning model. According to the algorithm stopping conditions, the unified federated learning framework outputs model parameters that can achieve a compromise between local and global performance.

Description

technical field [0001] The invention relates to a method for dynamic management of multi-dimensional resources of a heterogeneous network of unmanned aerial vehicles. Background technique [0002] With the rapid development of UAV autonomy and communication technology, UAV (Unmanned Aerial Vehicle, UAV) networking is more and more widely used in diverse civil, commercial and military scenarios, such as environmental monitoring, border surveillance , target tracking, emergency rescue, precision strike and other applications. For the rapid deployment and wide coverage of UAVs, the UAV heterogeneous network combines the advantages of the 5G cellular network and the point-to-point communication network, that is, the U2I (UAV-to-Infrastructure) transmission mode with the ground infrastructure as the relay can be used. Moreover, the UAV within the line-of-sight range can be flexibly transmitted in a U2U (UAV-to-UAV) mode in a link-through mode. In view of the highly dynamic and ...

AI Technical Summary

Problems solved by technology

[0006] (1) For UAV networks with highly dynamic topology changes, it is extremely challenging to obtain network-wide CSI in a centralized manner, and it is difficult to meet delay-sensitive service transmission requirements

[0007] (2) For resource management strategies based on channel state, if the estimated CSI does not match the actual CSI, the spectrum allocation results output by the algorithm may reduce network performance or fairness among users

However, as the number of UAV nodes and the scale of adjustable parameters continue to increase, the corresponding computational complexity and space complexity will increase exponentially. This solution is not suitable for dynamic UAV heterogeneous network scenarios

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