UAV heterogeneous network multi-dimensional resource dynamic management method

Abstract

The invention discloses a UAV heterogeneous network multi-dimensional resource dynamic management method, and aims to provide a network management strategy method capable of reducing the calculation complexity and improving the generalization of a learning model. According to the technical scheme, a unified federated learning architecture is composed of a ground base station centralized convergence layer and a UAV distributed execution layer, and a ground base station computing platform constructs a multi-agent enhanced learning model based on any UAV heterogeneous network scene and initializes model parameters; the distributed execution layer outputs a multi-dimensional resource management behavior by using a multi-agent reinforcement learning algorithm, obtains rewards and state transition feedback of a network environment to agent behaviors, and uploads model parameters to a local base station associated with the distributed execution layer. And the ground base station obtains all local model parameters through interaction and issues the updated parameters to each UAV learning model. According to algorithm stop conditions, a unified federated learning framework outputs model parameters capable of compromising and optimizing local and global performances.

Description

technical field [0001] The invention relates to a method for dynamically managing 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 and border surveillance. , target tracking, emergency rescue, precision strike and other applications. For the rapid deployment and wide coverage of UAVs, the UAV heterogeneous network integrates the advantages of 5G cellular networks and point-to-point communication networks, that is, it can adopt the U2I (UAV-to-Infrastructure) transmission mode with ground infrastructure as the relay, It is also possible to flexibly transmit data to UAVs within the line-of-sight range in a U2U (UAV-to-UAV) link-through manner. For the highly dynamic and complex aer...

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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