Vairavel G

@srmist.edu.in

Professor, Directorate of Learning and Development
SRM Institute of Science and Technology

Vairavel G


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https://researchid.co/guruvaira
is a Senior IEEE member and Professor in the Directorate of Learning and Development, SRM Institute of Science and Technology (University), India. His research interests are Massive MIMO Communication Systems, Antenna Design, Wireless Sensor Networks, Engineering Education, Instructional Technology, TPACK and CDIO based curricula design and implementations. He is presently serving as editor, guest editor for reputed journals and chaired reputed conferences. Presently he is a Governing Council Member of Nehru Institute of Engineering and Technology, Coimbatore. He is accredited as a Programme Leader for the Cambridge International Certification by Cambridge Assessment International Education. He delivered lectures on Outcome Based Education in various reputed institutes across India organized by Engineering Staff College of India (ESCI). He received IET Promotional Award from Chennai Local Network in the year 2019 and presently acting as Executive Member of IET Chennai Loc

EDUCATION

B.E.(ECE), M.E.(Communication Systems), Ph.D. (Wireless Communication)

RESEARCH INTERESTS

Massive MIMO Communication Systems, Antenna Design, Wireless Sensor Networks, Engineering Education, Instructional Technology, TPACK and CDIO based curricula design and implementations
27

Scopus Publications

187

Scholar Citations

8

Scholar h-index

7

Scholar i10-index

Scopus Publications

  • A Novel TLBO-Based Antenna Array Imperfection Calibration for Effective DOA Estimation in mmWave mMIMO Systems
    Aquino S., G. Vairavel
    International Journal of Antennas and Propagation, 2025
    Antenna Array imperfection calibration is an important concern in direction‐of‐arrival (DOA) estimation in 5 G millimeter Wave (mmWave) massive Multiple Input Multiple Output (mMIMO) systems. As the number of elements in mMIMO systems increases, array imperfections tend to increase, degrading the DOA estimation performance. Existing calibration techniques use the local optimum solution as the gain/phase, and the location error is high. The present work proposes an inter‐disciplinary learning teaching‐learning‐based optimization (IDL‐TLBO) to estimate the DOA. This algorithm exploits the joint sparse properties of the DOA vector and array perturbation matrix. Benefitting from inter‐disciplinary learning and sparse properties, the global search capability of the proposed method enhances the accuracy of DOA estimation. The efficacy of the proposed IDL‐TLBO was validated using various simulation scenarios. Simulation results reveal that the proposed method achieves a better performance‐complexity trade‐off than conventional methods for DOA estimation.
  • CHALLENGE BASED LEARNING: CULTIVATING SOCIAL RESPONSIBILTY IN A COMMUNITY CONNECT COURSE
    Proceedings of the International Cdio Conference, 2025
  • A Comprehensive Survey on 5G Network Slicing: Techniques, Challenges, and Reinforcement Learning Approaches
    Rajavel S, G Vairavel, Pradheep Balaji R
    2025 IEEE 1st International Conference on Innovations in Engineering and Next Generation Technologies for Sustainability Icinvents 2025, 2025
    The fifth-generation (5G) wireless network brings a significant shift in mobile communication by introducing network slicing, which allows multiple logical networks to operate over a single physical infrastructure. This capability enables service providers to deliver customized network performance for varied application categories such as enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communication (URLLC), and massive Machine-Type Communication (mMTC), each requiring specific levels of latency, reliability, and bandwidth. Leveraging technologies like Software-Defined Networking (SDN), Network Function Virtualization (NFV), and edge computing, slicing facilitates isolated, on-demand services across diverse use cases. Despite its advantages, the practical deployment of network slicing faces several hurdles, including slice orchestration, scalability, interference management, and optimal resource allocation. Traditional static solutions fall short in dynamic environments, prompting a shift towards intelligent, automated frameworks. In recent years, Machine Learning (ML), particularly Reinforcement Learning (RL), has been increasingly explored to enable adaptive and real-time slicing strategies. This paper presents a comprehensive survey of current developments in 5G network slicing, emphasizing architectural models, key technical barriers, optimization techniques, and RL-based solutions. It also discusses existing gaps, assesses real-world applicability, and outlines directions for future research.
  • TRANSFORMING TECHNICAL EDUCATION THROUGH INDUSTRY-INSTITUTE COLLABORATION ECOSYSTEM
    Proceedings of the International Cdio Conference, 2025
  • PHILOSOPHY OF ENGINEERING: A MOTIVATION COURSE FOR FRESHMEN ENGINEERING STUDENTS
    Proceedings of the International Cdio Conference, 2024
  • Systematic Literature Review on the Machine Learning Techniques for UAV-Assisted mm-Wave Communications
    M. S. Sugesh, G. Vairavel
    Lecture Notes in Electrical Engineering, 2024
  • DL-ADS: Improved Grey Wolf Optimization Enabled AE-LSTM Technique for Efficient Network Anomaly Detection in Internet of Thing Edge Computing
    J. Manokaran, G. Vairavel
    IEEE Access, 2024
    The Internet of Things (IoT) technology has begun to proliferate in recent years, which simultaneously increases the number of attacks. Owing to the massive volume and multi-dimensional data in IoT, anomaly detection leads to low prediction accuracy and a high false alarm rate. Further, there is a deficit of real-world test datasets for anomaly detection. This work aims to generate a novel real-time anomaly detection dataset and proposes an efficient anomaly detection model using an Improved Grey Wolf Optimization (IGWO)-enabled Long Short-Term Memory (LSTM) network in IoT edge scenarios. Dataset generation is carried out using a testbed setup containing Raspberry Pi 4 and sensors connected by a lightweight Message Queuing Telemetry Transport (MQTT) protocol. An autoencoder is used for feature reduction as it can investigate the input characteristics without sacrificing vital information. The LSTM classifier parameters, such as learning rate, optimizer, and batch size, are tuned precisely using IGWO techniques. The experimental results disclose that the proposed model achieves an accuracy of 99.11% for the testbed dataset, which is better than recent models. To confirm the generalizability of our model, the CICIDS 2017, DS2OS, and MQTTset standard datasets are applied explicitly. The developed model outcomes are statistically verified using the Wilcoxon signed rank test.
  • PPFCM-SMOTE: a novel balancing system for anomaly detection in IoT edge using probabilistic possibilistic fuzzy clustering and SMOTE
    J. Manokaran, G. Vairavel, J. Vijaya
    International Journal of Information Technology Singapore, 2024
  • Efficient Direction of Arrival Estimation in mMIMO Systems with Antenna Array Imperfections
    Aquino S, G. Vairavel
    Proceedings of the 8th International Conference on Communication and Electronics Systems Icces 2023, 2023
    Direction of arrival (DOA) estimation in 5G massive MIMO mm Wave systems, is crucial for effective beamforming. A high-resolution DOA estimate is required to avoid interferences and steer the beam to the corresponding user. The accuracy of DOA estimation deteriorates significantly due to the antenna array imperfections, channel fluctuations, and noise. This research investigates the performance of DOA estimation with a uniform linear array of 128 elements at a 5G Frequency Range FR2 of 26GHz and a single antenna user. The antenna array imperfections considered are antenna position perturbations, inconsistent gain, and phases. To cope with these array imperfections, the pre-processing technique is done by evaluating the compensation matrix with the help of the Least Square (LS) approach and Orthogonal Matching Pursuit (OMP). The compensation matrices are estimated with the steering vectors of ideal and practical array manifolds. Then the received signals are compensated for imperfections with the compensation matrix. The compensated signals are then fed into the DOA estimator, and the performance analysis of Multiple Signal Classifier (MUSIC), Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT), Root-MUSIC (RMUSIC), and Beam Scan algorithms are compared. The quantitative results and analytical formulation of the estimation technique are discussed based on the results of the DOA estimation techniques. Simulation results show that resolution of the DOA estimation of Root-MUSIC and ESPRIT with OMP achieves better performance-complexity trade-off at low SNR region, whereas MUSIC and Beam scan achieves better performance-complexity trade-off at high SNR region than LS compensation technique.
  • IGWO-SoE: Improved Grey Wolf Optimization Based Stack of Ensemble Learning Algorithm for Anomaly Detection in Internet of Things Edge Computing
    J. Manokaran, G. Vairavel
    IEEE Access, 2023
    With the tremendous growth and popularization of the Internet of Things (IoT), the number of attacks targeting such devices has also increased. Therefore, enhancing the anomaly detection model to maximize detection accuracy and mitigate cyber-attacks in time-critical IoT edge scenarios is essential. Furthermore, there is a lack of vivid, precise, cross-layered, and diverse datasets in IoT for evaluating these anomaly detection models. This paper aims to develop an improved anomaly detection model based on an optimized stacked ensemble learning algorithm at edge computing. Initially, a novel synthetic dataset with multiple cross-layer attacks is generated using the Cooja simulator to train our proposed model. In addition, by introducing an improved grey wolf optimization (IGWO) approach, the parameters of ensemble learning algorithms, such as number of trees, learning rate, and sample rate, are tuned precisely, and the stacking ensemble concept is applied to the optimized ensemble learning algorithms to enhance their prediction capabilities. The experimental results demonstrate that the developed model produces a detection accuracy of 99.44% for our proposed Cooja simulated dataset, which is higher than the contemporary methods. The generalizability of the proposed model is expressed explicitly using four different datasets: NSL KDD, UNSW NB 15, MQTTset, and CICIDS 2017. Finally, we assess the befitting of the proposed model using a chi-square statistical significance test, thereby providing an enriched contribution to the recent works in anomaly detection.
  • TEACHING COMPETENCY DEVELOPMENT FRAMEWORK FOR SRMIST FACULTY MEMBERS
    Proceedings of the International Cdio Conference, 2023
  • GIWRF-SMOTE: Gini impurity-based weighted random forest with SMOTE for effective malware attack and anomaly detection in IoT-Edge
    J Manokaran, Gurusami Vairavel
    Smart Science, 2023
  • A Review of Direction of Arrival Estimation Techniques in Massive MIMO 5G Wireless Communication Systems
    S. Aquino, G. Vairavel
    Lecture Notes in Electrical Engineering, 2023
  • A Novel Set Theory Rule based Hybrid Feature Selection Techniques for Efficient Anomaly Detection System in IoT Edge
    J Manokaran, G Vairavel, J Vijaya
    Iq Cchess 2023 2023 IEEE International Conference on Quantum Technologies Communications Computing Hardware and Embedded Systems Security, 2023
  • A Compact Planar Monopole UWB MIMO Antenna Design with Increased Isolation for Diversity Applications
    S. Kolangiammal, L. Balaji, G. Vairavel
    Applied Computational Electromagnetics Society Journal, 2022
  • Smart Anomaly Detection Using Data-Driven Techniques in IoT Edge: A Survey
    J. Manokaran, G. Vairavel
    Lecture Notes in Electrical Engineering, 2022
  • An Empirical Comparison of Machine Learning Algorithms for Attack Detection in Internet of Things Edge
    Manokaran J, Vairavel G
    Ecs Transactions, 2022
  • Understanding textile antenna by reviewing and simulating it for high data rates applications
    Asit Kittur, G. Vairavel
    Advances in Intelligent Systems and Computing, 2021
  • OPTIMIZED ENERGY USING CENTRALIZED CLUSTERING PROTOCOL IN HETEROGENEOUS WIRELESS SENSOR NETWORKS
    Arpn Journal of Engineering and Applied Sciences, 2021
  • Compact planar monopole uwb mimo antenna for diversity applications
    S. Kolangiammal, G. Vairavel
    Advances in Intelligent Systems and Computing, 2021
  • Preface
    Advances in Intelligent Systems and Computing, 2021
  • Novel cuff button antenna for dual-band applications
    R. Sreelakshmy, G. Vairavel
    ICT Express, 2019
  • RF energy harvesting using a single band cuff button rectenna
    R. Sreelakshmy, G. Vairavel
    Lecture Notes in Electrical Engineering, 2019
  • A comparative study on the performance of wearable antennas using flexible and non-flexible substrates
    R Sreelakshmy, G Vairavel
    Journal of Computational and Theoretical Nanoscience, 2018
  • Performance analysis of an uplink MISO-CDMA system using multistage multi-user detection scheme with V-BLAST signal detection algorithms
    Journal of Theoretical and Applied Information Technology, 2014
  • VLSI implementation of image scaling processor
    Priya P. Gowthami, G. Vairavel
    2014 International Conference on Electronics and Communication Systems Icecs 2014, 2014
  • VLSI implementation of reconfigurable processing modulefor binary and grayscale image processing
    S. Dinesh, G. Vairavel
    2014 International Conference on Electronics and Communication Systems Icecs 2014, 2014

RECENT SCHOLAR PUBLICATIONS

  • A Comprehensive Survey on 5G Network Slicing: Techniques, Challenges, and Reinforcement Learning Approaches
    S Rajavel, G Vairavel
    2025 IEEE First International Conference on Innovations in Engineering and … , 2025
    2025
  • A Novel TLBO-Based Antenna Array Imperfection Calibration for Effective DOA Estimation in mmWave mMIMO Systems
    S Aquino, G Vairavel
    International Journal of Antennas and Propagation 2025 , 2025
    2025
    Citations: 1
  • PPFCM-SMOTE: a novel balancing system for anomaly detection in IoT edge using probabilistic possibilistic fuzzy clustering and SMOTE
    J Manokaran, G Vairavel, J Vijaya
    International Journal of Information Technology, 1-20 , 2024
    2024
    Citations: 16
  • Dl-ads: Improved grey wolf optimization enabled ae-lstm technique for efficient network anomaly detection in internet of thing edge computing
    J Manokaran, G Vairavel
    IEEE Access 12, 75983-76002 , 2024
    2024
    Citations: 18
  • Igwo-soe: Improved grey wolf optimization based stack of ensemble learning algorithm for anomaly detection in internet of things edge computing
    J Manokaran, G Vairavel
    IEEE Access 11, 106934-106953 , 2023
    2023
    Citations: 20
  • A novel set theory rule based hybrid feature selection techniques for efficient anomaly detection system in IoT edge
    J Manokaran, G Vairavel, J Vijaya
    2023 International Conference on Quantum Technologies, Communications … , 2023
    2023
    Citations: 5
  • Systematic Literature Review on the Machine Learning Techniques for UAV-Assisted mm-Wave Communications
    MS Sugesh, G Vairavel
    International Conference on Electrical and Electronics Engineering, 517-534 , 2023
    2023
    Citations: 1
  • Efficient Direction of Arrival Estimation in mMIMO Systems with Antenna Array Imperfections
    S Aquino, G Vairavel
    2023 8th International Conference on Communication and Electronics Systems … , 2023
    2023
    Citations: 2
  • GIWRF-SMOTE: Gini impurity-based weighted random forest with SMOTE for effective malware attack and anomaly detection in IoT-Edge
    J Manokaran, G Vairavel
    Smart Science 11 (2), 276-292 , 2023
    2023
    Citations: 30
  • A Review of Direction of Arrival Estimation Techniques in Massive MIMO 5G Wireless Communication Systems
    S Aquino, G Vairavel
    Proceedings of Fourth International Conference on Communication, Computing … , 2023
    2023
    Citations: 8
  • A compact planar monopole UWB MIMO antenna design with increased isolation for diversity applications
    S Kolangiammal, L Balaji, G Vairavel
    Applied Computational Electromagnetics Society Journal (ACES), 458-465 , 2022
    2022
    Citations: 3
  • An empirical comparison of machine learning algorithms for attack detection in internet of things edge
    J Manokaran, G Vairavel
    ECS Transactions 107 (1), 2403 , 2022
    2022
    Citations: 13
  • Smart anomaly detection using data-driven techniques in iot edge: a survey
    J Manokaran, G Vairavel
    Proceedings of Third International Conference on Communication, Computing … , 2022
    2022
    Citations: 16
  • e-PRACTICE ENVIRONMENT TO LEARN PROGRAMMING FOR PROBLEM SOLVING COURSE
    S Rajeev, G Vairavel
    Proceedings of the 17th International CDIO Conference, hosted online by … , 2021
    2021
  • OPTIMIZED ENERGY USING CENTRALIZED CLUSTERING PROTOCOL IN HETEROGENEOUS WIRELESS SENSOR NETWORKS
    G Bhuvaneswari, C.A., Vairavel
    ARPN Journal of Engineering and Applied Sciences 16 (2), 215 - 223 , 2021
    2021
    Citations: 6
  • Compact planar monopole UWB MIMO antenna for diversity applications
    S Kolangiammal, G Vairavel
    Advances in Smart System Technologies: Select Proceedings of ICFSST 2019 … , 2020
    2020
    Citations: 5
  • Understanding textile antenna by reviewing and simulating it for high data rates applications
    A Kittur, G Vairavel
    Advances in Smart System Technologies: Select Proceedings of ICFSST 2019 … , 2020
    2020
    Citations: 5
  • A Comparative Study on the Performance of Wearable Antennas Using Flexible and Non-Flexible Substrates
    GV R Sreelakshmi
    Journal of Computational and Theoretical Nanoscience 15 (11/12), 3481 - 3485 , 2018
    2018
  • RF Energy Harvesting Using a Single Band Cuff Button Rectenna
    R Sreelakshmy, G Vairavel
    International Conference on Communications and Cyber Physical Engineering … , 2018
    2018
    Citations: 1
  • Novel cuff button antenna for dual band applications
    GV R Sreelakshmi
    ICT Express - Elsevier , 2018
    2018
    Citations: 37

MOST CITED SCHOLAR PUBLICATIONS

  • Novel cuff button antenna for dual band applications
    GV R Sreelakshmi
    ICT Express - Elsevier , 2018
    2018
    Citations: 37
  • GIWRF-SMOTE: Gini impurity-based weighted random forest with SMOTE for effective malware attack and anomaly detection in IoT-Edge
    J Manokaran, G Vairavel
    Smart Science 11 (2), 276-292 , 2023
    2023
    Citations: 30
  • Igwo-soe: Improved grey wolf optimization based stack of ensemble learning algorithm for anomaly detection in internet of things edge computing
    J Manokaran, G Vairavel
    IEEE Access 11, 106934-106953 , 2023
    2023
    Citations: 20
  • Dl-ads: Improved grey wolf optimization enabled ae-lstm technique for efficient network anomaly detection in internet of thing edge computing
    J Manokaran, G Vairavel
    IEEE Access 12, 75983-76002 , 2024
    2024
    Citations: 18
  • PPFCM-SMOTE: a novel balancing system for anomaly detection in IoT edge using probabilistic possibilistic fuzzy clustering and SMOTE
    J Manokaran, G Vairavel, J Vijaya
    International Journal of Information Technology, 1-20 , 2024
    2024
    Citations: 16
  • Smart anomaly detection using data-driven techniques in iot edge: a survey
    J Manokaran, G Vairavel
    Proceedings of Third International Conference on Communication, Computing … , 2022
    2022
    Citations: 16
  • An empirical comparison of machine learning algorithms for attack detection in internet of things edge
    J Manokaran, G Vairavel
    ECS Transactions 107 (1), 2403 , 2022
    2022
    Citations: 13
  • A Review of Direction of Arrival Estimation Techniques in Massive MIMO 5G Wireless Communication Systems
    S Aquino, G Vairavel
    Proceedings of Fourth International Conference on Communication, Computing … , 2023
    2023
    Citations: 8
  • OPTIMIZED ENERGY USING CENTRALIZED CLUSTERING PROTOCOL IN HETEROGENEOUS WIRELESS SENSOR NETWORKS
    G Bhuvaneswari, C.A., Vairavel
    ARPN Journal of Engineering and Applied Sciences 16 (2), 215 - 223 , 2021
    2021
    Citations: 6
  • A novel set theory rule based hybrid feature selection techniques for efficient anomaly detection system in IoT edge
    J Manokaran, G Vairavel, J Vijaya
    2023 International Conference on Quantum Technologies, Communications … , 2023
    2023
    Citations: 5
  • Compact planar monopole UWB MIMO antenna for diversity applications
    S Kolangiammal, G Vairavel
    Advances in Smart System Technologies: Select Proceedings of ICFSST 2019 … , 2020
    2020
    Citations: 5
  • Understanding textile antenna by reviewing and simulating it for high data rates applications
    A Kittur, G Vairavel
    Advances in Smart System Technologies: Select Proceedings of ICFSST 2019 … , 2020
    2020
    Citations: 5
  • A compact planar monopole UWB MIMO antenna design with increased isolation for diversity applications
    S Kolangiammal, L Balaji, G Vairavel
    Applied Computational Electromagnetics Society Journal (ACES), 458-465 , 2022
    2022
    Citations: 3
  • Efficient Direction of Arrival Estimation in mMIMO Systems with Antenna Array Imperfections
    S Aquino, G Vairavel
    2023 8th International Conference on Communication and Electronics Systems … , 2023
    2023
    Citations: 2
  • A Novel TLBO-Based Antenna Array Imperfection Calibration for Effective DOA Estimation in mmWave mMIMO Systems
    S Aquino, G Vairavel
    International Journal of Antennas and Propagation 2025 , 2025
    2025
    Citations: 1
  • Systematic Literature Review on the Machine Learning Techniques for UAV-Assisted mm-Wave Communications
    MS Sugesh, G Vairavel
    International Conference on Electrical and Electronics Engineering, 517-534 , 2023
    2023
    Citations: 1
  • RF Energy Harvesting Using a Single Band Cuff Button Rectenna
    R Sreelakshmy, G Vairavel
    International Conference on Communications and Cyber Physical Engineering … , 2018
    2018
    Citations: 1
  • A Comprehensive Survey on 5G Network Slicing: Techniques, Challenges, and Reinforcement Learning Approaches
    S Rajavel, G Vairavel
    2025 IEEE First International Conference on Innovations in Engineering and … , 2025
    2025
  • e-PRACTICE ENVIRONMENT TO LEARN PROGRAMMING FOR PROBLEM SOLVING COURSE
    S Rajeev, G Vairavel
    Proceedings of the 17th International CDIO Conference, hosted online by … , 2021
    2021
  • A Comparative Study on the Performance of Wearable Antennas Using Flexible and Non-Flexible Substrates
    GV R Sreelakshmi
    Journal of Computational and Theoretical Nanoscience 15 (11/12), 3481 - 3485 , 2018
    2018