Abnormal signal semi-supervised classification method and system, and data processing terminal

Abstract

The invention relates to the technical field of deep learning and wireless communication spectrum signals, and discloses an abnormal signal semi-supervised classification method and system, and a data processing terminal. The method comprises the following steps: establishing a deep clustering model, taking abnormal signal data as input of a CNN model, and then extracting compression features of input data as input of a K-means clustering algorithm for clustering; meanwhile, inputting the features extracted by the CNN into a classification layer of the CNN for classification; and finally, calculating the loss between the output of the K-means and the output of the CNN, and updating the parameters of the CNN until the iteration process is converged, so as to achieve the purpose of using the clustering result to assist in training the classifier. In order to enable the model to have better performance on a data set, optimization methods of a pre-training model, determining an initial centroid of clustering, constructing a category mean value Memory, replacing a pseudo tag and the like are introduced; in addition, the adopted semi-supervised learning method can enable spectrum management personnel to classify abnormal signals under the condition of small user interaction.

Description

technical field [0001] The invention relates to the technical field of deep learning and wireless communication spectrum signals, in particular to a semi-supervised classification method, system and data processing terminal for abnormal signals. Background technique [0002] The radio spectrum is one of our most precious and widely used natural resources. With the development of wireless communication technology, wireless communication networks include various types of communication systems for diverse user communication services, while the use of spectrum becomes very complicated, which leads to problems such as radio wave congestion and other interference. The wireless network structure is complex and diverse, and there are many types of anomalies in the wireless spectrum signal. In order to facilitate the spectrum management of the wireless network structure, researchers usually hope to know the type of the anomaly and what kind of abnormal signal it is after detecting th...

AI Technical Summary

Problems solved by technology

[0005](1) It takes a lot of manpower and financial resources to label the abnormal wireless spectrum abnormal signals, and it is very difficult to implement. Therefore, the supervised The learned classification method is not suitable for wireless spectrum anomaly signals

[0006](2) Since the wireless network environment is very complex in the real world, there are many kinds of abnormal signals in the wireless spectrum. Clustering is also difficult to achieve the results we want

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