Machine learning-based physical layer channel authentication method

Abstract

The invention discloses a physical layer channel authentication method based on machine learning. The method comprises the steps of S1, collecting the information packet channel information (CSI) of alegal information sender A and a simulated illegal information sender E by means of a receiver B; S2, pre-processing the information packet channel information of the legal information sender A and the simulated illegal information sender E by the receiver B; S3, generating a binary training data set by the receiver B; S4, based on the classification algorithm in the machine learning algorithm, carrying out the training by the receiver B according to the binary training data set to generate a classifier; S5, judging whether a received information packet is legal or not by the receiver B by utilizing the classifier, and realizing the authentication of the information packet. The invention provides the physical layer channel authentication method based on machine learning. The method is suitable for authentication equipment and authentication scenes with limited resources. The method has the advantages of low calculation complexity and high authentication accuracy.

Description

technical field [0001] The invention relates to physical layer channel authentication, in particular to physical layer channel authentication based on machine learning. Background technique [0002] In the future wireless communication system, the number of miniature terminal devices pouring into the wireless network will increase exponentially. During the process of accessing the wireless network, the miniature terminal needs to establish security measures such as secure access authentication. However, at present, traditional access authentication is mainly based on cryptographic technology, and there are many kinds of micro terminal devices, some of which are very simple, such as wearable devices and simple Internet of Things terminals, which cannot provide complex computing resources. Edge computing nodes have With certain data processing and storage capabilities, access authentication between asymmetric resources is difficult to use traditional password-based authentica...

AI Technical Summary

Problems solved by technology

However, T A presents a non-random distribution, the threshold η A Difficult to determine, thus affecting the authentication accuracy

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