Data of novel features and FS_DT-SSVM

Data of novel features and FS_DT-SSVM

Citation Author(s):
Xiaolin
Zhang
College of Information and Communication Engineering, Harbin Engineering University
Jianting
Sun
College of Information and Communication Engineering, Harbin Engineering University
Xiaotong
Zhang
College of Information and Communication Engineering, Harbin Engineering University
Submitted by:
Xiaotong Zhang
Last updated:
Tue, 12/03/2019 - 22:12
DOI:
10.21227/0m92-z416
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Abstract: 

This dataset has 11 sets of feature data extracted with different signal-to-noise and a set of simulation results of modulation classification. Feature dataset consists of 2 novel features and 15 classic features, and dataset of simulation results represents the effectiveness of FS_DT-SSVM classifier.

These data match the experimental data of the paper “Automatic Modulation Classification Based on Novel Feature Extraction Algorithms”.

Instructions: 

Instruction of Dataset Document

This file has 11 sets of data with different signal-to-noise and a set of simulation results of modulation classification.

The dataset file SSVM_n10dB includes the features data extracted for training and testing classifier when the signal-to-noise is -10dB.

The dataset file SSVM_n7dB includes the features data extracted for training and testing classifier when the signal-to-noise is -7dB.

The dataset file SSVM_n4dB includes the features data extracted for training and testing classifier when the signal-to-noise is -4dB.

The dataset file SSVM_n1dB includes the features data extracted for training and testing classifier when the signal-to-noise is -1dB.

The dataset file SSVM_2dB includes the features data extracted for training and testing classifier when the signal-to-noise is 2dB.

The dataset file SSVM_5dB includes the features data extracted for training and testing classifier when the signal-to-noise is 5dB.

The dataset file SSVM_8dB includes the features data extracted for training and testing classifier when the signal-to-noise is 8dB.

The dataset file SSVM_11dB includes the features data extracted for training and testing classifier when the signal-to-noise is 11dB.

The dataset file SSVM_14dB includes the features data extracted for training and testing classifier when the signal-to-noise is 14dB.

The dataset file SSVM_17dB includes the features data extracted for training and testing classifier when the signal-to-noise is 17dB.

The dataset file SSVM_20dB includes the features data extracted for training and testing classifier when the signal-to-noise is 20dB.

 

In these files, word “char_” that appears in the name of dataset represents that the dataset consists of three cyclic spectral features.

In these files, word “cum_” that appears in the name of dataset represents that the dataset consists of five high-order cumulant features.

In these files, word “PF_” that appears in the name of dataset represents that the dataset consists of novel feature, differential nonlinear phase peak factor.

In these files, word “test_Char_” that appears in the name of dataset represents that the dataset consists of eight instantaneous features.

Dataset Files

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[1] Xiaolin Zhang, Jianting Sun, Xiaotong Zhang, "Data of novel features and FS_DT-SSVM", IEEE Dataport, 2019. [Online]. Available: http://dx.doi.org/10.21227/0m92-z416. Accessed: Dec. 14, 2019.
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url = {http://dx.doi.org/10.21227/0m92-z416},
author = {Xiaolin Zhang; Jianting Sun; Xiaotong Zhang },
publisher = {IEEE Dataport},
title = {Data of novel features and FS_DT-SSVM},
year = {2019} }
TY - DATA
T1 - Data of novel features and FS_DT-SSVM
AU - Xiaolin Zhang; Jianting Sun; Xiaotong Zhang
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Xiaolin Zhang, Jianting Sun, Xiaotong Zhang. (2019). Data of novel features and FS_DT-SSVM. IEEE Dataport. http://dx.doi.org/10.21227/0m92-z416
Xiaolin Zhang, Jianting Sun, Xiaotong Zhang, 2019. Data of novel features and FS_DT-SSVM. Available at: http://dx.doi.org/10.21227/0m92-z416.
Xiaolin Zhang, Jianting Sun, Xiaotong Zhang. (2019). "Data of novel features and FS_DT-SSVM." Web.
1. Xiaolin Zhang, Jianting Sun, Xiaotong Zhang. Data of novel features and FS_DT-SSVM [Internet]. IEEE Dataport; 2019. Available from : http://dx.doi.org/10.21227/0m92-z416
Xiaolin Zhang, Jianting Sun, Xiaotong Zhang. "Data of novel features and FS_DT-SSVM." doi: 10.21227/0m92-z416