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An Iterative Extraction Method of Communication Signal Graph Domain Features Based on KL Divergence

A KL divergence and communication signal technology, applied in the field of signal processing, can solve the problems of cumbersome calculation, heavy workload, and affecting the recognition effect

Active Publication Date: 2020-09-18
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However at AMC G The entire graph domain feature construction in is done manually, the calculation is very cumbersome, and the workload is heavy. If the feature sequence is not properly selected, it is easy to cause a large error, which usually affects the recognition effect.

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  • An Iterative Extraction Method of Communication Signal Graph Domain Features Based on KL Divergence
  • An Iterative Extraction Method of Communication Signal Graph Domain Features Based on KL Divergence
  • An Iterative Extraction Method of Communication Signal Graph Domain Features Based on KL Divergence

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Embodiment

[0055] For the convenience of description, the relevant technical terms appearing in the specific implementation are explained first:

[0056] BPSK (binary phase-shift keying): binary phase-shift keying;

[0057] QPSK (quadrature phase-shift keying): quadrature phase-shift keying;

[0058] OQPSK (offset quadrature phase-shift keying): offset quadrature phase-shift keying;

[0059] 2FSK(binary frequency-shift keying): binary frequency shift keying;

[0060] 4FSK(quadrature frequency-shift keying): quadrature frequency-shift keying;

[0061] MSK (minimum shift keying): minimum frequency shift keying;

[0062] LB (Likelihood-based influence): based on maximum likelihood

[0063] FB (feature-based): based on features

[0064] FE (feature-extraction): feature extraction

[0065] PR (pattern recognition): pattern recognition

[0066] AMC G (graph-based automatic modulation classification): Automatic modulation classification based on graph domain;

[0067] KL divergence (Ku...

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Abstract

The invention discloses an iterative extraction method of communication signal graph domain features based on KL divergence, which uses the cyclic spectrum of communication signals to realize the automatic construction of feature sequences under the premise of ensuring the robustness of the algorithm; specifically, the invention first Through the map domain mapping theory, the cyclic spectrum of the communication signal is converted into a series of adjacency matrices, and all elements in the adjacency matrix are extracted to construct a candidate set of feature sequences; Add the KL divergence of an index relative to other modulation types to obtain the KL divergence belonging to the modulation type, and determine the order of feature extraction according to the KL divergence of each modulation type; select the KL divergence in sequence The largest index is used as the feature of the corresponding modulation type, and each time a feature is extracted, it is deleted from the feature sequence candidate set until the feature sequences of all modulation types are constructed.

Description

technical field [0001] The invention belongs to the technical field of signal processing, and more specifically relates to a method for iteratively extracting communication signal graph domain features based on KL divergence. Background technique [0002] Automatic modulation classification (AMC) can identify the modulation type of a received signal with little or no prior knowledge and is widely used in military and civilian communications. Typical automatic modulation recognition methods are usually divided into two categories: maximum likelihood based methods (ML) and feature extraction based methods (FB). The method based on maximum likelihood is a theory based on hypothesis testing. Through the likelihood function of the received signal, the likelihood ratio is compared with a threshold value to make a decision. This method can obtain the most Excellent solution, but there are also many disadvantages; the method based on feature recognition includes two stages of featu...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04L27/00
CPCH04L27/0012
Inventor 阎啸王茜张国玉吴孝纯刘冠男
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA