使用主动学习的调制和信号类别标记以及使用机器学习的分类
Modulation and signal class labelling using active learning and classification using machine learning
摘要：
机器学习（ML）中的监督学习需要标记数据集。进一步的实时数据分类需要一种易于获得的标记方法。无线调制和信号分类在军事、商业、电子侦察、认知无线电等领域有着广泛的应用。本文主要研究了基于主动学习框架的实时无线调制和信号类别标注问题。进一步的调制和信号分类是用机器学习算法，如KNN，SVM，朴素贝叶斯。主动学习有助于用最少的数据样本来标记属于不同类的数据点。对于信噪比为18 dB的信号，主动学习算法的准确率为86%。此外，基于KNN的调制和信号分类模型在信噪比范围内表现良好，对18 dB信号的准确率为99.8%。这项工作的新颖性存在于应用主动学习无线调制和信号类标签。调制和信号类别都是在给定的时间标记的帮助下，从数据样本的偶联形成。
英文摘要：
Supervised learning in machine learning (ML) requires labelled data set. Further real-time data classification requires an easily available methodology for labelling. Wireless modulation and signal classification find their application in plenty of areas such as military, commercial and electronic reconaissance and cognitive radio. This paper mainly aims to solve the problem of real-time wireless modulation and signal class labelling with an active learning framework. Further modulation and signal classification is performed with machine learning algorithms such as KNN, SVM, Naive bayes. Active learning helps in labelling the data points belonging to different classes with the least amount of data samples trained. An accuracy of 86 percent is obtained by the active learning algorithm for the signal with SNR 18 dB. Further, KNN based model for modulation and signal classification performs well over range of SNR, and an accuracy of 99.8 percent is obtained for 18 dB signal. The novelty of this work exists in applying active learning for wireless modulation and signal class labelling. Both modulation and signal classes are labelled at a given time with help of couplet formation from the data samples.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
