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基于优化的高阶累积量线性组合的自动调制识别。

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

机构信息

Faculty of Engineering & Computer Sciences, National University of Modern Languages, Islamabad 44000, Pakistan.

Department of Engineering and Applied Sciences, University of Bergamo, 24129 Bergamo, Italy.

出版信息

Sensors (Basel). 2022 Oct 2;22(19):7488. doi: 10.3390/s22197488.

DOI:10.3390/s22197488
PMID:36236583
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9571176/
Abstract

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.

摘要

自动调制识别（AMR）由于物联网（IoT）和相关通信技术的日益普及，已在从通用通信到许多军事应用等各种领域得到应用。在这篇研究文章中，我们提出了一个创新的想法，即将计算累积量的线性组合（LC）的经典数学技术与遗传算法（GA）相结合，以创建超累积量。然后，使用 K 最近邻（KNN）法将这些超累积量用于对衰落信道上的五种数字调制方案进行分类。我们提出的分类器在使用较小样本量时，在较低 SNR 下显著提高了识别精度的百分比。与现有技术的比较表明了我们提出的分类器的优越性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a93/9571176/0b073df8ae60/sensors-22-07488-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a93/9571176/007c8fdb59c0/sensors-22-07488-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a93/9571176/37fbff63fd47/sensors-22-07488-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a93/9571176/d5aca1035342/sensors-22-07488-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a93/9571176/741be3906a63/sensors-22-07488-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a93/9571176/9ba06f99bbd6/sensors-22-07488-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a93/9571176/cdcf2733d689/sensors-22-07488-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a93/9571176/0b073df8ae60/sensors-22-07488-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a93/9571176/007c8fdb59c0/sensors-22-07488-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a93/9571176/37fbff63fd47/sensors-22-07488-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a93/9571176/d5aca1035342/sensors-22-07488-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a93/9571176/741be3906a63/sensors-22-07488-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a93/9571176/9ba06f99bbd6/sensors-22-07488-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a93/9571176/cdcf2733d689/sensors-22-07488-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a93/9571176/0b073df8ae60/sensors-22-07488-g007.jpg

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