论文标题
使用机器学习使用主动学习和分类的调制和信号类标记
Modulation and signal class labelling using active learning and classification using machine learning
论文作者
论文摘要
机器学习中的监督学习（ML）需要标记的数据集。进一步的实时数据分类需要一种易于使用的标签方法。无线调制和信号分类在许多领域找到了它们的应用，例如军事，商业和电子回收和认知无线电。本文主要旨在通过主动学习框架解决实时无线调制和信号类标签的问题。通过机器学习算法（例如KNN，SVM，Naive Bayes）进行进一步的调制和信号分类。积极的学习有助于标记属于不同类别的数据点，并用培训的数据样本最少。通过使用SNR 18 dB的信号的主动学习算法获得了86％的精度。此外，基于KNN的调制和信号分类模型在SNR范围内表现良好，对于18 dB信号，获得了99.8％的精度。这项工作的新颖性存在于将主动学习应用于无线调制和信号类标记。调制和信号类在给定时间标记在数据样本中的对联形成的情况下。
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.
