Faculty Publications
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Publications by NITK Faculty
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Item Deep Learning Approach for Wireless Signal and Modulation Classification(Institute of Electrical and Electronics Engineers Inc., 2021) B C, B.; Deshmukh, A.; Rupa, M.V.; Sirigina, R.P.; Vankayala, S.K.; Narasimhadhan, A.V.This paper aims to classify signal and modulation classes of a given wireless signal with high accuracy using a model having a low number of parameters. We propose an end-to-end method to classify a wireless signal based on its signal and modulation type using a CNN-based architecture. The proposed architecture is similar to that of the LeNet-5. Firstly, we implement signal and modulation classification using a decision tree, followed by a random forest algorithm, classic examples of machine learning(ML) based algorithms. Since our dataset is a time series, we also implement using RNN-LSTM based model for the classification. The proposed model has fewer parameters than that of the CNN-based, RNN-LSTM based architectures. Moreover, it achieves better accuracy for a wide range of signal-to-noise ratios than a decision tree, random forest, RNN-LSTM based classifiers. © 2021 IEEE.Item Mitigating Neighborship Attack In Underwater Sensor Networks(Institute of Electrical and Electronics Engineers Inc., 2021) Deshmukh, A.; Deo, S.; Chandavarkar, B.R.Transmission of information through Underwater Wireless Sensor Networks(UWSN) across the ocean is one of the enabling technologies for underwater communication. These advances trigger security concerns of the underlying UWSN. Due to the Sack of predictability of the movement of the nodes in such a system, secure neighbour discovery for successful information exchange is a challenge. A neighborship attack is the one which hinders neighbour discovery amongst the various nodes within the network. The wormhole attack and the Sybil attack being the prominent attacks in this category, lead to various issues if not mitigated. The consequences of these attacks can quickly scale from reduced throughput to loss of confidentiality. Moreover, conventional cryptographic algorithms are not possible to implement in a UWSN due to restrictions on the open acoustic channel and severe underwater conditions. In this paper, we propose a true-neighbour algorithm for mitigating neighborship attack in UWSN. Furthermore, the performance of this algorithm is demonstrated in UnetStack with reference to end to end packet delay, with and without implementation of the algorithm. © 2021 IEEE.Item Modulation and signal class labelling with active learning and classification using machine learning(Institute of Electrical and Electronics Engineers Inc., 2022) Bhargava, B.C.; Deshmukh, A.; Narasimhadhan, A.V.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 reconnaissance 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 perform well over a 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. © 2022 IEEE.