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Communication Interference Existence Detection Method Based on Residual Connection and Dilated Convolution

A technology for communication interference and detection methods, applied in neural learning methods, transmission monitoring, biological neural network models, etc., can solve the problems of high pressure of artificially constructing features, affecting the correct transmission of useful signals, and low accuracy of interference monitoring. The effect of enhancing generalization performance, breaking asymmetry, and maintaining original data resolution

Active Publication Date: 2022-01-28
CENT SOUTH UNIV
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  • Abstract
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  • Application Information

AI Technical Summary

Problems solved by technology

[0002] With the continuous development of current communication equipment, the complex and dynamic electromagnetic environment has brought great challenges to the design of broadband wireless communication systems
Due to the relatively wide coverage of the operating spectrum of the broadband wireless communication system, there may be other signals that are complexly interleaved in the external environment, which will further increase the probability of the system being interfered
A variety of interference signals will directly affect the correct transmission of useful signals and reduce the efficiency of signal transmission
[0003] In the original communication field, the interference detection in the prior art requires the extraction of a large number of artificial interference features, and the pressure of artificially constructing features is relatively high, resulting in low accuracy and slow detection speed of interference monitoring in the prior art

Method used

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  • Communication Interference Existence Detection Method Based on Residual Connection and Dilated Convolution
  • Communication Interference Existence Detection Method Based on Residual Connection and Dilated Convolution
  • Communication Interference Existence Detection Method Based on Residual Connection and Dilated Convolution

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Experimental program
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Effect test

Embodiment 1

[0059] A method for detecting the existence of communication interference based on residual connection and dilated convolution, including the following steps:

[0060] Step 1. Construct the interference presence detection data set, and simulate the system signals under different interference-to-noise ratios and the system signals with interference;

[0061] Step 2, establishing an interference existence detection model for detecting interference in system signals; the interference existence detection model is based on residual connection and dilated convolution;

[0062] Step 3, using the interference presence detection data set described in step 1 to train the interference presence detection model in step 2, so that it converges to obtain the final interference presence detection model;

[0063] Step 4, collect the real communication data, and obtain the power spectrum data of the real communication data, input it into the final interference existence detection model, and obt...

Embodiment 2

[0107] This embodiment adopts the detection accuracy between the interference existence detection model based on residual connection and dilated convolution in embodiment 1 and the neural network based on multi-layer fully connected layers, the neural network based on convolution and the neural network based on cyclic neural network Compared.

[0108] In this embodiment, a simulation of interference presence detection is performed on signal power spectrum data under different interference-to-noise ratios, and the selected interference-to-noise ratio range JNR=[-10:1:10]dB.

[0109] The neural network based on multi-layer fully connected layers mainly uses 4, 5, and 6 layers of fully connected neural networks. The number of neurons in each layer of the 4-layer network is [256, 128, 64, 1], and the number of neurons in each layer of the 5-layer network is [512,256,128,64,1], the number of neurons in each layer of the 6-layer network is [512,256,128,64,32,1], the activation funct...

Embodiment 3

[0117] This embodiment is a simulation comparison of prediction speeds between different models in Embodiment 2.

[0118] Test the models trained by each interference detection model on 3000 verification sets, set the batch size to 32, record the total running time, and calculate the number of predicted samples per second for each interference detection model, and the results of the number of predicted samples per second are shown in the table 1.

[0119] Table 1 Comparison of the number of samples predicted per second by different neural networks

[0120] Interference Detection Presence Detection Model Prediction samples per second 4 layers of fully connected layers (MLP_3) 27961 5 layers of fully connected layers (MLP_4) 23697 6 layers of fully connected layers (MLP_5) 19394 Interference Detection Model Based on Convolutional Neural Network (CNN) 843 Interference Detection Model (BiLSTM) Based on Recurrent Neural Network 382 Int...

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Abstract

The present invention provides a method for detecting the existence of communication interference based on residual connection and dilated convolution, including the following steps: Step 1, constructing an interference existence detection data set, and simulating system signals and interference with different interference-to-noise ratios system signal; step 2, establish an interference existence detection model for detecting interference in the system signal; the interference existence detection model is based on residual connection and dilated convolution; step 3, use the interference described in step 1 The existence detection data set trains the interference existence detection model in step 2, so that it converges, and obtains the final interference existence detection model. The invention has the characteristics of automatic extraction of interference features in signals, high accuracy of interference detection and fast interference detection speed.

Description

technical field [0001] The invention specifically relates to a method for detecting the existence of communication interference based on residual connection and dilated convolution. Background technique [0002] With the continuous development of current communication equipment, the complex and dynamic electromagnetic environment has brought great challenges to the design of broadband wireless communication systems. Since the operating frequency spectrum of the broadband wireless communication system covers a relatively wide range, there may be other signals that are complexly interleaved in the external environment, which will further increase the probability of the system being interfered. A variety of interference signals will directly affect the correct transmission of useful signals and reduce the efficiency of signal transmission. [0003] In the original communication field, the interference detection in the prior art requires the extraction of a large number of arti...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04B17/345H04B17/391G06N3/04G06N3/08
CPCH04B17/345H04B17/3912G06N3/08G06N3/048G06N3/045
Inventor 李芳芳任星凯张健张伟
Owner CENT SOUTH UNIV
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