Direct symbol decoding using GA-SVM in chaotic baseband wireless communication system

DOI10.1016/j.jfranklin.2021.06.012zbMath1485.94028arXiv2103.10855OpenAlexW3137501532MaRDI QIDQ2047072

Hai-Peng Ren, Hui-Ping Yin

Publication date: 19 August 2021

Published in: Journal of the Franklin Institute (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2103.10855

zbMATH Keywords

information retrievalgenetic algorithm support vector machinesymbol detection method

Mathematics Subject Classification ID

Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Communication theory (94A05)

Uses Software

• LIBSVM
• svmachines
• PRMLT

Cites Work

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• Scalable Gaussian kernel support vector machines with sublinear training time complexity
• Kernel methods in machine learning
• High dimensional data classification and feature selection using support vector machines
• A new approach to communications using chaotic signals
• Effective Sequential Classifier Training for SVM-Based Multitemporal Remote Sensing Image Classification
• REVIEW OF CHAOS COMMUNICATION BY FEEDBACK CONTROL OF SYMBOLIC DYNAMICS
• A matched filter for chaos
• Chaos in optimal communication waveforms

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