Automatic Modulation Recognition Based on the Optimized Linear Combination of Higher-Order Cumulants

被引：5

作者：

Hussain, Asad ^{[1
,2
]}

Alam, Sheraz ^{[1
]}

Ghauri, Sajjad A. ^{[3
]}

Ali, Mubashir ^{[4
]}

Sherazi, Husnain Raza ^{[5
]}

Akhunzada, Adnan ^{[6
]}

Bibi, Iram ^{[7
]}

Gani, Abdullah ^{[8
]}

机构：

[1] Natl Univ Modern Languages, Fac Engn & Comp Sci, Islamabad 44000, Pakistan

[2] Univ Bergamo, Dept Engn & Appl Sci, I-24129 Bergamo, Italy

[3] ISRA Univ, Sch Engn & Appl Sci, Islamabad Campus, Islamabad 44000, Pakistan

[4] Univ Bergamo, Dept Management Informat & Prod Engn, I-24129 Bergamo, Italy

[5] Univ West London, Sch Comp & Engn, London W5 5RF, England

[6] Univ Doha Sci & Technol, Coll Comp & Informat Technol, Doha 24449, Qatar

[7] Comsats Univ, Dept Comp Sci, Islamabad 45550, Pakistan

[8] Univ Malaysia Sabah, Fac Comp & Informat, Kota Kinabalu 88400, Sabah, Malaysia

来源：

SENSORS | 2022年 / 22卷 / 19期

关键词：

modulation recognition; K-nearest neighbor; genetic algorithm; higher-order cumulants; CLASSIFICATION; ARCHITECTURE;

D O I：

10.3390/s22197488

中图分类号：

O65 [分析化学];

学科分类号：

070302 ; 081704 ;

摘要：

Automatic modulation recognition (AMR) is used in various domains-from general-purpose communication to many military applications-thanks to the growing popularity of the Internet of Things (IoT) and related communication technologies. In this research article, we propose an innovative idea of combining the classical mathematical technique of computing linear combinations (LCs) of cumulants with a genetic algorithm (GA) to create super-cumulants. These super-cumulants are further used to classify five digital modulation schemes on fading channels using the K-nearest neighbor (KNN). Our proposed classifier significantly improves the percentage recognition accuracy at lower SNRs when using smaller sample sizes. A comparison with existing techniques manifests the supremacy of our proposed classifier.

引用

页数：16

共 63 条

[51]

Triantafyllakis K., 2017, P IEEE INT S DYN SPE, P1, DOI DOI 10.1109/DYSPAN.2017.7920749

[52]

Usman M, 2020, I C INF COMM TECH CO, P288, DOI 10.1109/ICTC49870.2020.9289261

[53] Deep cascading network architecture for robust automatic modulation classification [J].

Weng, Lintianran ;

He, Yuan ;

Peng, Jianhua ;

Zheng, Jianchao ;

Li, Xinyu .

NEUROCOMPUTING, 2021, 455 (455) :308-324

[54] Robust Automatic Modulation Classification Under Varying Noise Conditions [J].

Wu, Zhilu ;

Zhou, Siyang ;

Yin, Zhendong ;

Ma, Bo ;

Yang, Zhutian .

IEEE ACCESS, 2017, 5 :19733-19741

[55] A Combination Strategy of Feature Selection Based on an Integrated Optimization Algorithm and Weighted K-Nearest Neighbor to Improve the Performance of Network Intrusion Detection [J].

Xu, Hui ;

Przystupa, Krzysztof ;

Fang, Ce ;

Marciniak, Andrzej ;

Kochan, Orest ;

Beshley, Mykola .

ELECTRONICS, 2020, 9 (08) :1-22

[56]

Yang Zhang, 2020, 2020 IEEE 9th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), P1056, DOI 10.1109/ITAIC49862.2020.9338946

[57] Optimal power allocation for NOMA-enabled D2D communication with imperfect SIC decoding [J].

Yu, Shanshan ;

Khan, Wali Ullah ;

Zhang, Xiaoqing ;

Liu, Ju .

PHYSICAL COMMUNICATION, 2021, 46 (46)

[58] Deep Learning for Robust Automatic Modulation Recognition Method for IoT Applications [J].

Zhang, Tingping ;

Shuai, Cong ;

Zhou, Yaru .

IEEE ACCESS, 2020, 8 :117689-117697

[59] y Automatic Modulation Classification Based on Novel Feature Extraction Algorithms [J].

Zhang, Xiaolin ;

Sun, Jianting ;

Zhang, Xiaotong .

IEEE ACCESS, 2020, 8 :16362-16371

[60]

Zhao XD, 2020, INT CONF WIRE COMMUN, P823, DOI 10.1109/WCSP49889.2020.9299797

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