Modulation and signal class labelling using active learning and classification using machine learning
This addresses the need for efficient data labelling in wireless applications such as military and cognitive radio, but it is incremental as it applies existing active learning and ML methods to a specific domain.
The paper tackles real-time wireless modulation and signal class labelling using an active learning framework, achieving 86% accuracy for labelling at 18 dB SNR, and applies machine learning algorithms like KNN for classification, with KNN reaching 99.8% accuracy at the same SNR.
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.