Gaurav Jajoo

Bio: Gaurav Jajoo is an academic researcher from Indian Institute of Technology, Jodhpur. The author has contributed to research in topics: QAM & Phase-shift keying. The author has an hindex of 4, co-authored 8 publications receiving 68 citations. Previous affiliations of Gaurav Jajoo include Indian Institutes of Technology.

Automatic Modulation Classification Based on Constellation Density Using Deep Learning

Abstract: Deep learning (DL) is a newly addressed area of research in the field of modulation classification. In this letter, a constellation density matrix (CDM) based modulation classification algorithm is proposed to identify different orders of ASK, PSK, and QAM. CDM is formed through local density distribution of the signal’s constellation generated using LabVIEW for a wide range of SNR. Two DL models, ResNet-50 and Inception ResNet V2 are trained through color images formed by filtering the CDM. Classification accuracy achieved demonstrates better performance compared to many existing classifiers in the literature.

Blind signal modulation recognition through clustering analysis of constellation signature

Abstract: Modulation classification using OPTICS clustering algorithm has been proposed.Algorithm is reliably classifying 4ASK, 8ASK, BPSK, QPSK and 8QAM above 9dB SNR.Classification is unsupervised and generalized for any orders ASK, PSK and QAM.Algorithm is implemented and tested on real time RF signal using labVIEW. Automatic recognition of digital modulation schemes is becoming an active research area in many covert operations. It has many military applications where surveillance and electronic warfare requires a type of modulation in intercepted signal to prepare jamming signals. Most of the approaches are based on modulated signal's component, but the modulation type can be best identified with the use of constellation diagram. The proposed technique is able to recognize M-QAM, M-ASK, and M-PSK modulation scheme in Additive White Gaussian Noise (AWGN) environment. As the constellation points form clusters in the I-Q plane, the order of the modulation can be obtained by estimating the correct number of clusters, which is calculated by OPTICS algorithm. The least square error has been calculated using linear regression from the obtained constellation points, to identify either ASK or PSK and QAM. The error is least for ASK which differentiates ASK from PSK and QAM. To identify between the PSK and QAM, k-means clustering is employed to find the number of centroids equal to order of modulation estimated by OPTICS. With the difference in maximum and minimum absolute value of the centroids, PSK or QAM is recognized. The proposed method shows an improvement in the classification accuracy which reaches 100% using 1024 symbols at 20dB compared to 98.89%, 98.05%, and 98% when using more complex classifiers like Support Vector Machine, Naive Bayes Classifier, KNN respectively. The method used is unsupervised whereas most of the methods in the literature require training phase to set the thresholds or weights for final model to detect modulation type. This algorithm is also implemented in LabVIEW, and tested on real-time signals. An intelligent system is made which does not require any knowledge of symbol rate, carrier frequency, and any training phase to set thresholds, and detects the type of modulation blindly in real time. Modulated RF signals are generated by NI PXIe-5673 (RF transmitter). NI PXI 5600 is used to downconvert RF signal and NI PXI-5142 (100 MS/s OSP digitizer) is used to sample the downverted signal.

Blind Signal PSK/QAM Recognition Using Clustering Analysis of Constellation Signature in Flat Fading Channel

Abstract: A novel method based on constellation structure is proposed to identify PSK and QAM modulation of different orders, in the slow and flat fading channel. The proposed method does not require training for threshold optimization and considers carrier frequency, symbol rate, and phase offset unknown. The symbol rate is estimated using the spectrum of the instantaneous phase of the complex baseband signal. Carrier frequency offset (CFO) is estimated and corrected from the downconverted signal and downsampled to the estimated symbol rate for extraction of constellation points. The phase offset is determined based on the symmetrical structure of constellation. The features extracted using k-medoids are used for classification of the final modulation scheme. Results show that the proposed algorithm outperforms some existing classifiers and offers lower computational complexity compared to algorithms based on subtractive clustering.

Modulation scheme detection of blind signal using constellation graphical representation

Abstract: Modulation scheme detection of blind signal is vital area of research now a days because of the requirement in security and communication applications. In this paper, modulation class is identified based on the constellation graphical representation. Carrier frequency, symbol rate and phase offset are the essential parameters required for the extraction of constellation diagram, which are calculated efficiently. Efficient way of Classification between ASK, PSK and QAM is proposed in this paper. ASK and pool of PSK and QAM is separated using linear regression, and further classification between PSK and QAM has done using circle fitting.

Blind Signal Digital Modulation Classification through k-medoids Clustering

Abstract: Modulation scheme classifier for the received RF signal is proposed in this paper. Modulation classification is an intermediate step between data detection and its demodulation for extracting the final information. Proposed method estimates the carrier frequency offset after downconversion of passband signal with estimated carrier frequency and corrects for it. Signal is sampled with high frequency and symbol rate is estimated. Sampled signal is downsampled to estimated symbol rate to extract the constellation points. For identification of modulation scheme between QAM and PSK of different orders, k-medoids clustering is used. Blindly, k medoids are estimated for k equals to 4, 8, 16 and 64 and similarity to ideal constellation structure is calculated. Final decision for modulation scheme is given in favor for which similarity with ideal constellation structure is maximum. The simulation results for the method shows that different modulation schemes are classified efficiently above 10 dB SNR in presence of Additive White Gaussian Noise. Method proposed is unsupervised and has low computational complexity.

Automatic Modulation Classification Based on Constellation Density Using Deep Learning

Abstract: Deep learning (DL) is a newly addressed area of research in the field of modulation classification. In this letter, a constellation density matrix (CDM) based modulation classification algorithm is proposed to identify different orders of ASK, PSK, and QAM. CDM is formed through local density distribution of the signal’s constellation generated using LabVIEW for a wide range of SNR. Two DL models, ResNet-50 and Inception ResNet V2 are trained through color images formed by filtering the CDM. Classification accuracy achieved demonstrates better performance compared to many existing classifiers in the literature.

Machine Learning Based Automatic Modulation Recognition for Wireless Communications: A Comprehensive Survey

Abstract: The rapid development of information and wireless communication technologies together with the large increase in the number of end-users have made the radio spectrum more crowded than ever. Besides, providing a stable and reliable service is challenging, as electromagnetic environments are evolving and becoming more sophisticated. Accordingly, there is an urgent need for more reliable and intelligent communication systems that can improve the spectrum efficiency and the quality of service to provide agile management of network resources, so as to better meet the needs of future wireless users. Specifically, Automatic Modulation Recognition (AMR) plays an essential role in most intelligent communication systems especially with the emergence of Software Defined Radio (SDR). AMR is an indispensable task while performing spectrum sensing in Cognitive Radio (CR). Thanks to the significant advancements in Deep Learning (DL) applications, new and powerful tools have been provided which can tackle problems in this space. Thus, today, integrating DL models into AMR has gained the attention of many researchers. This work aims to provide a comprehensive state-of-the-art review of the most recent Machine Learning (ML) based AMR methods for Single-Input Single-Output (SISO) and Multiple-Input Multiple-Output (MIMO) systems. Furthermore, the architecture of each model will be identified along with a detailed comparison in terms of specifications and performance. Finally, an outline of the open problems, challenges, and potential research directions is provided along with discussion and conclusion.

A comparison of clustering algorithms for automatic modulation classification

Abstract: In this paper, the k-means, k-medoids, fuzzy c-means, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Ordering Points To Identify the Clustering Structure (OPTICS), and hierarchical clustering algorithms (with the addition of the elbow method) are examined for the purpose of Automatic Modulation Classification (AMC). This study compares these algorithms in terms of classification accuracy and execution time for either estimating the modulation order, determining centroid locations, or both. The best performing algorithms are combined to provide a simple AMC method which is then evaluated in an Additive White Gaussian Noise (AWGN) channel with M-Quadrature Amplitude Modulation (QAM) and M-Phase Shift Keying (PSK). Such an AMC method does not rely on any thresholds to be set by a human or machine learning algorithm, resulting in a highly flexible system. The proposed method can be configured to not give false positives, making it suitable for applications such as spectrum monitoring and regulatory enforcement.

Blind Signal PSK/QAM Recognition Using Clustering Analysis of Constellation Signature in Flat Fading Channel

Abstract: A novel method based on constellation structure is proposed to identify PSK and QAM modulation of different orders, in the slow and flat fading channel. The proposed method does not require training for threshold optimization and considers carrier frequency, symbol rate, and phase offset unknown. The symbol rate is estimated using the spectrum of the instantaneous phase of the complex baseband signal. Carrier frequency offset (CFO) is estimated and corrected from the downconverted signal and downsampled to the estimated symbol rate for extraction of constellation points. The phase offset is determined based on the symmetrical structure of constellation. The features extracted using k-medoids are used for classification of the final modulation scheme. Results show that the proposed algorithm outperforms some existing classifiers and offers lower computational complexity compared to algorithms based on subtractive clustering.

A Survey of Modulation Classification Using Deep Learning: Signal Representation and Data Preprocessing

Abstract: Modulation classification is one of the key tasks for communications systems monitoring, management, and control for addressing technical issues, including spectrum awareness, adaptive transmissions, and interference avoidance. Recently, deep learning (DL)-based modulation classification has attracted significant attention due to its superiority in feature extraction and classification accuracy. In DL-based modulation classification, one major challenge is to preprocess a received signal and represent it in a proper format before feeding the signal into deep neural networks. This article provides a comprehensive survey of the state-of-the-art DL-based modulation classification algorithms, especially the techniques of signal representation and data preprocessing utilized in these algorithms. Since a received signal can be represented by either features, images, sequences, or a combination of them, existing algorithms of DL-based modulation classification can be categorized into four groups and are reviewed accordingly in this article. Furthermore, the advantages as well as disadvantages of each signal representation method are summarized and discussed.