Wireless signal detection and electromagnetic interference classification system and method based on deep learning

Abstract

The invention discloses a wireless signal detection and electromagnetic interference classification system and method based on deep learning. The method comprises the steps of obtaining the observation data by using the frequency spectrum monitoring nodes deployed in a distributed manner; executing two types of signal feature mining in parallel based on the complex value observation data to obtaina wireless signal detection data set and an electromagnetic interference classification data set, training two groups of convolutional neural networks in parallel based on the two types of data sets,and then detecting wireless signals and executing electromagnetic interference classification by using the two groups of trained convolutional neural networks respectively. The system and the methodhave the beneficial effects that the accuracy of the wireless signal detection and the electromagnetic interference classification can be improved; the generalization singular value decomposition andthe space division are performed on two types of data sets, so that the additive noise can be eliminated, the crosstalk from adjacent channels can be inhibited, and the authenticity of data can be enhanced; and the wireless signal detection and the electromagnetic interference classification are carried out concurrently, so that the efficiency is high, and the response is fast.

Description

technical field [0001] The present invention relates to the field of wireless communication technology, in particular to a system and method for wireless signal detection and electromagnetic interference classification based on deep learning. Background technique [0002] Reasonable planning and strict supervision of wireless spectrum are the key to promote the orderly and efficient use of wireless spectrum resources. In a complex wireless communication environment, distributed wireless spectrum monitoring must be performed in a wide-band, wide-area range, resulting in a flood of spectrum monitoring data. In order to be able to extract valuable spectrum usage information from massive and complex spectrum monitoring data sets, it is necessary to design accurate and efficient analysis, identification and classification algorithms. Through the intelligent monitoring and analysis of spectrum resources in the wireless communication environment, it can not only provide technical ...

AI Technical Summary

Problems solved by technology

However, the above methods need to use specific signal features and basic data models, but how to select signal features and how to build basic data models still face challenges

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