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Small sample AMC method based on Transform coding network

A coding network and small sample technology, applied in the field of small sample AMC, can solve the problem that hundreds or even more modulation signals are required to be identified, and achieve the effect of improving the recognition performance and the recognition accuracy.

Pending Publication Date: 2022-06-03
XIDIAN UNIV
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Problems solved by technology

[0005] For the problems existing in the prior art, the purpose of the present invention is to provide a small-sample AMC method based on Transformer encoding network, which solves the problem that each type of modulated signal to be identified needs hundreds or even more training samples in the traditional classification method, Enables efficient classification of modulated signals with a small number of labeled samples in each class

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  • Small sample AMC method based on Transform coding network
  • Small sample AMC method based on Transform coding network
  • Small sample AMC method based on Transform coding network

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Embodiment Construction

[0017] The embodiments of the present invention will be described in detail below in conjunction with the examples, but those skilled in the art will understand that the following examples are only used to illustrate the present invention and should not be regarded as limiting the scope of the present invention.

[0018] refer to figure 1 , a small sample AMC method based on Transformer coding network, including the following steps:

[0019] Step 1: Obtain a training sample set and a test sample set; select a training support set and a training query set from the training sample set, and select a test support set and a test query set from the test sample set;

[0020] Specifically, the samples of this embodiment are selected from the RadioML data set;

[0021] The training sample set contains M types of modulation signals, and each type of modulation signal contains 10 different signal-to-noise ratios (the 10 signal-to-noise ratios are 0dB, 2dB, 4dB, 6dB, 8dB, 10dB, 12dB, 14d...

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Abstract

The invention relates to the technical field of frequency spectrum monitoring, in particular to a small sample AMC (Automatic Modulation Classification) method based on a Transform coding network. According to the invention, the Transform coding network can map a sample from an original signal space to an embedding space which is easy to classify, so that the recognition performance is improved; according to the method, the recognition accuracy close to that of a traditional AMC method can be obtained only through a small number of training samples, and the recognition precision is effectively improved under the condition that modulation signal samples are limited.

Description

technical field [0001] The invention relates to the technical field of spectrum monitoring, in particular to a small sample AMC (Automatic Modulation Classification) method based on a Transformer coding network. Background technique [0002] AMC technology is used to identify the modulation type of an unknown signal, and is widely used in areas such as signal detection, spectrum sharing, and interference identification. [0003] AMC techniques can generally be divided into two categories: decision theory-based and pattern recognition-based methods. The method based on decision theory obtains the recognition result by comparing the likelihood functions of different modulated signals. This method has high complexity and poor robustness, and is difficult to deploy in practice. Compared with the method based on decision theory, the method based on pattern recognition is less complex and easier to implement, so it has become the mainstream method of AMC. [0004] With the devel...

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Application Information

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IPC IPC(8): H04L27/00H04B17/336G06K9/62G06N3/04G06N3/08
CPCH04L27/0012H04B17/336G06N3/08G06N3/045G06F18/2413Y04S10/50
Inventor 周峰张慧王力石晓然白雪茹
Owner XIDIAN UNIV