  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.
