Hybrid access cognitive radio network slice resource allocation method and device

Abstract

The invention provides a hybrid access cognitive radio network slice resource allocation method and device, and the method comprises the steps: constructing an intelligent agent for a cognitive user, enabling a state to correspond to a state needing interaction or not needing interaction, and enabling an action to correspond to transmission power and a channel; inputting the state of the intelligent agent into a graph convolutional neural network model of a hybrid access cognitive wireless network scene so as to calculate attention heads for neighbor cognitive users, and outputting action related information of the intelligent agent through a convolutional layer after all the attention heads are connected; based on a transmission power prediction result and an occupied channel prediction result of the cognitive user, judging whether the action meets a communication time delay requirement, a transmission rate requirement and an interference temperature threshold value requirement, and calculating a value of a reward function; if not, punishing the reward function; and distributing network slice resources for cognitive users in the hybrid access cognitive radio network by using the trained neural network model and the intelligent agent. Through the method, the spectrum use efficiency is improved.

Description

technical field [0001] The present invention relates to the technical field of wireless communication, and in particular to a hybrid access cognitive wireless network slice resource allocation method and device. Background technique [0002] With the development of information technology, the industrial model has also changed, and spectrum resources have become more and more precious. On the one hand, with the continuous development of emerging wireless communication services, spectrum resources are becoming increasingly tight; on the other hand, the existing inefficient legacy systems have low utilization efficiency of spectrum resources and are difficult to transform. In the past decade, the use of wireless devices such as vehicles, mobile phones, tablets, and various wireless sensors has increased rapidly, which has promoted the development of the fifth generation mobile networks (5G). In 5G wireless networks, the data rate is expected to be 10 times higher than the curr...

AI Technical Summary

Problems solved by technology

[0006] However, the complexity of practical application problems is very high, and the amount of information is also large. It may not be possible to grasp the global information at the same time, so the single-agent reinforcement learning algorithm is no longer applicable.

Images