Anand Kumar

Anand Kumar (Graduate Student Member, IEEE) received his B.Tech degree in Electronics and Communication Engineering from Dr. A.P.J. Abdul Kalam Technical University, UP, India, in 2015, and his M.Tech degree in Telecommunication Engineering from NIT Durgapur, India, in 2018. He completed his Ph.D. in Signal Processing for Wireless Communication from the Department of Electrical Engineering at the Indian Institute of Technology, Patna, India, in 2024. He is currently a Research Associate in the Department of Electrical Engineering at the Indian Institute of Science, Bangalore, India. His research interests include signal processing for wireless communication.

EDUCATION

Ph.D in Signal Processing from Indian Institute of Technology Patna

RESEARCH, TEACHING, or OTHER INTERESTS

Electrical and Electronic Engineering, Artificial Intelligence, Signal Processing, Engineering

Scopus Publications

Triple Attention-Aided Vision Transformer Based AMC for RIS-Assisted MIMO-OFDM Systems Under System Impairment
Anand Kumar, Sudhan Majhi
IEEE Communications Letters, 2025
In this letter, we present automated modulation classification (AMC) for reconfigurable intelligent surface (RIS)-assisted multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems under imperfect channel state information (CSI), residual carrier frequency offset (CFO) and symbol time offset (STO) errors. We leverage a triple attention-aided vision transformer (TrpViT) architecture, which uses a vision-centric approach within the transformer network to enhance global information acquisition. The TrpViT is implemented by utilizing three complementary attention mechanisms spatial, dilated, and channel attention in a unique attention block. This unique attention block extracts spatially local features while expanding the scope to capture more comprehensive signal features. The adopted attention mechanisms effectively capture long-range spatial dependencies and channel interactions within input signals by optimizing the model complexity. The performance of the proposed method is compared against existing models and it has been demonstrated that the proposed method accurately classifies higher modulation schemes for RIS-assisted MIMO-OFDM systems. The computational complexity of the proposed model is also compared with the existing state-of-the-art.
Automatic Modulation Classification for OFDM Systems Using Bi-Stream and Attention-Based CNN-LSTM Model
Anand Kumar, Mahesh Shamrao Chaudhari, Sudhan Majhi
IEEE Communications Letters, 2024
Existing deep learning (DL) models for automatic modulation classification (AMC) of orthogonal frequency division multiplexing (OFDM) signals consider only spatial or temporal attributes, ignoring feature interaction, which limits their ability to classify higher-order modulation formats. To address this, we propose a bi-stream and attention-based convolutional neural network and long short-term memory network (CNN-LSTM) for AMC. It can efficiently extract special and temporal features from the in-phase and quadrature (IQ) samples and the amplitude and phase (AP) of the received signal for AMC. Each stream consists of a CNN, LSTM, and attention module to extract features from signal patterns, which improves the classification performance. The proposed AMC can classify M-ary phase-shift keying (M-PSK) and higher-order M-ary quadrature amplitude modulation (M-QAM) formats in the presence of randomized carrier frequency offset, symbol timing offset, phase offset, and unknown channel state information. The proposed model outperforms existing models in terms of classification accuracy and complexity. Finally, the proposed model has been validated on the real-time dataset generated by the radio frequency testbed.
Residual Stack-Aided Hybrid CNN-LSTM-Based Automatic Modulation Classification for Orthogonal Time-Frequency Space System
Anand Kumar, Manish, Udit Satija
IEEE Communications Letters, 2023
In this letter, for the first time, we propose an automatic modulation classification (AMC) method for orthogonal time-frequency space (OTFS) signal modulation using a hybrid convolutional neural network and long short-term memory (CNN-LSTM) network with a residual stack. The proposed method uses in-phase and quadrature-phase (IQ) of the received OTFS modulated signal to classify the received modulation accurately. We consider the six digital modulation schemes such as binary phase shift keying (BPSK), quadrature PSK (QPSK), minimum-shift keying (MSK), on-off keying (OOK), 4-amplitude shift keying (4ASK), and 8ASK for orthogonal time-frequency space (OTFS) in the delay-Doppler domain. Results depict that the proposed method achieves a high classification performance even at a low signal-to-noise ratio (SNR).
Automatic Modulation Classification for Adaptive OFDM Systems Using Convolutional Neural Networks With Residual Learning
Anand Kumar, Keerthi Kumar Srinivas, Sudhan Majhi
IEEE Access, 2023
Automatic modulation classification (AMC) is becoming a promising technique for future adaptive wireless transceiver systems. The existing blind modulation classification (BMC) methods for orthogonal frequency division multiplexing (OFDM) fail to achieve the required performance by using statistical-based methods. Thus, the modulation classification research community is trying to adopt the deep learning (DL) method to improve the modulation classification accuracy. However, most of the existing DL methods for AMC of OFDM that involve the extraction of statistical features from the signal do not work for adaptive transceiver systems where the signal parameters are changed dynamically. In this paper, we design and implement AMC for adaptive OFDM systems by using a convolutional neural network (CNN) with residual learning. The proposed AMC can identify the modulation format of the received OFDM signal with different number of subcarriers, randomized carrier frequency offset (CFO), symbol timing offset (STO), phase offset, and unknown channel state information. We use residual learning to mitigate the effect of varying CFO, STO, and AWGN noise on the received OFDM signal. A larger pool of modulation schemes such as binary phase-shift keying (BPSK), quadrature PSK (QPSK), offset QPSK, $\\pi $ /4-QPSK, minimum shift keying, 8-PSK, 16-quadrature amplitude modulation (QAM), and 64-QAM are being considered for the proposed AMC for OFDM system in a dynamic environment. The performance and complexity of the proposed AMC are compared with the existing statistical feature-based and DL-based approaches. The proposed AMC for the OFDM system is also verified on the real-time data set generated from the universal software radio peripheral testbed setup.
A Survey of Blind Modulation Classification Techniques for OFDM Signals
Anand Kumar, Sudhan Majhi, Guan Gui, Hsiao-Chun Wu, Chau Yuen
Sensors, 2022
Blind modulation classification (MC) is an integral part of designing an adaptive or intelligent transceiver for future wireless communications. Blind MC has several applications in the adaptive and automated systems of sixth generation (6G) communications to improve spectral efficiency and power efficiency, and reduce latency. It will become a integral part of intelligent software-defined radios (SDR) for future communication. In this paper, we provide various MC techniques for orthogonal frequency division multiplexing (OFDM) signals in a systematic way. We focus on the most widely used statistical and machine learning (ML) models and emphasize their advantages and limitations. The statistical-based blind MC includes likelihood-based (LB), maximum a posteriori (MAP) and feature-based methods (FB). The ML-based automated MC includes k-nearest neighbors (KNN), support vector machine (SVM), decision trees (DTs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) based MC methods. This survey will help the reader to understand the main characteristics of each technique, their advantages and disadvantages. We have also simulated some primary methods, i.e., statistical- and ML-based algorithms, under various constraints, which allows a fair comparison among different methodologies. The overall system performance in terms bit error rate (BER) in the presence of MC is also provided. We also provide a survey of some practical experiment works carried out through National Instrument hardware over an indoor propagation environment. In the end, open problems and possible directions for blind MC research are briefly discussed.
Design and Implementation of Blind Modulation Classification for Asynchronous MIMO-OFDM System
Amit Kumar Pathy, Anand Kumar, Rahul Gupta, Sushant Kumar, Sudhan Majhi
IEEE Transactions on Instrumentation and Measurement, 2021
In this article, we design and implement a tree-based blind modulation classification algorithm for asynchronous multiple-input–multiple-output and orthogonal frequency-division multiplexing (MIMO-OFDM) systems. It can classify many of the linearly modulated signals, such as binary phase shift keying (BPSK), quadrature phase shift keying (QPSK), offset QPSK, minimum shift keying, and 16-quadrature amplitude modulation. The proposed classifier works in the presence of unknown frequency, timing, and phase offsets and with no prior knowledge of channel state information. Classification is performed in three steps. In the first step, preprocessing is done on the received signal to nullify the effect of timing offset. In the second step, key features are extracted by calculating higher order cumulants of the frequency-domain signal. In the third step, thresholds are determined by using the likelihood ratio test. A closed-form theoretical derivation for the probability of correct classification is obtained. The Monte Carlo simulations are conducted to compare the performance of the proposed algorithm with the existing algorithms. Finally, the proposed algorithm is validated through radio frequency testbed measurements over an indoor propagation environment.