# Modulation and signal class labelling using active learning and classification using machine learning

arXiv: https://arxiv.org/abs/2202.12930 · HTML: https://mlsanity.com/paper/2202.12930/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 15 references that are in the map.
- First posted on arXiv 23 Feb 2022.

## Builds on (most cited first, 3 of 3)

- [Active Transfer Learning Network: A Unified Deep Joint Spectral-Spatial Feature Learning Model For Hyperspectral Image Classification](https://mlsanity.com/paper/1904.02454/) (2019)
- [Learning of Time-Frequency Attention Mechanism for Automatic Modulation Recognition](https://mlsanity.com/paper/2111.03258/) (2021)
- [Improving performance of aircraft detection in satellite imagery while limiting the labelling effort: Hybrid active learning](https://mlsanity.com/paper/2202.04890/) (2022)

## Built on by (most cited first, 2 of 2)

- [DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning](https://mlsanity.com/paper/2507.12011/) (2025)
- [Pilot selection in the era of Virtual reality: algorithms for accurate and interpretable machine learning models](https://mlsanity.com/paper/2510.03345/) (2025)

## Abstract (arXiv)

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.