Radio signal clustering method and system based on deep learning

Abstract

The invention discloses a radio signal clustering method based on deep learning, and the method comprises the steps: S1, adjusting the sizes of sample data of a second data set and a first data set to be consistent, and dividing the sample data of the two data sets into a plurality of batches containing the same number of sample data; S2, constructing a depth model, and inputting the adjusted second data set batch sample data into the depth model for pre-training until the model training is stable; S3, performing clustering training; and S4, outputting a clustering result. The invention further comprises a radio signal clustering system based on deep learning. According to the method, a neural network combining one-dimensional convolution and two-dimensional convolution is established, and a modulation signal data set is used for pre-training a depth model, so that the depth model tends to extract features related to modulation types, and during subsequent clustering, the model is guided to cluster samples according to the modulation types. The method has good universality in the aspect of signal clustering.

Description

technical field [0001] The invention relates to the field of unsupervised learning of radio signals, in particular to a radio signal clustering method and system based on deep learning. Background technique [0002] Radio refers to electromagnetic waves that travel in free space. The radio communication technology currently used is to convert information such as sound, text, data, and images into electrical signals, and load the radio signal onto the radio wave after modulation. The radio wave is transmitted in space, and is received by the receiving end together with the radio signal carried. take over. In daily life, using radio signals to transmit information is still the mainstream way of modern information transmission. After the radio signal is received, its specific modulation type needs to be identified, and then the corresponding demodulation can be performed according to the modulation type, and the information carried by the signal can be extracted. People ofte...

AI Technical Summary

Problems solved by technology

Therefore, this method is only suitable for distinguishing single-frequency signal SF and BPSK, QPSK, 16QAM signals, and cannot distinguish more types of radio signals

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