Dr. Abhishek Mondal

About

Abhishek Mondal received his B.Tech. degree in electronics and communication engineering from Jalpaiguri Government Engineering College, West Bengal, India in 2015, M.Tech. degree in Communication and Information Technology from National Institute of Technology Srinagar, Jammu & Kashmir, India in 2017, and Ph.D. degree in Electronics and Communication Engineering from the National Institute of Technology Silchar, Assam, India in 2023. He is an Assistant Professor at the Department of Electronics and Communication Engineering, National Institute of Technology Calicut, Kozhikode, Kerala, India. Before that, he was an Artificial Intelligence Engineer at AATWRI Aerospace and Defence Private Limited, Chennai, India from January 2023 to October 2023. His current research interests include unmanned aerial vehicle-based wireless communication, IoT networks, device-to-device communication, energy harvesting in wireless networks, resource allocation in cognitive radio, backscattering, physical layer security, and deep reinforcement learning. He is also a potential reviewer of IEEE Transactions on Communications, IEEE Transactions on Intelligent Vehicles, IEEE Transactions on Green Communications and Networking, IEEE Transactions on Mobile Computing, IEEE Wireless Communication Letters, IEEE Communication Letters, IEEE Access, IET Communications, Transactions on Emerging Telecommunications Technologies, Telecommunication Systems, Cluster Computing, Scientific Report, and various international conferences.

Employment

• National Institute of Technology Calicut Assistant Professor
  2023 - Present

Education

Education history is unavailable.

Projects & Funding

Projects & funding information is unavailable.

Publications (8)

• Practical IRS-Assisted 6G Wireless Communications: Tight Ergodic Capacity Approximations with Coupling and Amplitude-Phase Distortion
  Wireless Personal Communications 2026 DOI: 10.1007/s11277-026-11913-9
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• Multiagent Reinforcement Learning for Offloading Cellular Communications With Cooperating UAVs
  IEEE Transactions on Aerospace and Electronic Systems 2025 DOI: 10.1109/TAES.2025.3554150
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• Joint Trajectory, User-Association, and Power Control for Green UAV-Assisted Data Collection Using Deep Reinforcement Learning
  IEEE Transactions on Intelligent Vehicles 2025 DOI: 10.1109/TIV.2024.3416067
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• Optimal deployment strategy of airships for near‐space communication system using multiobjective advanced evolutionary algorithm
  International Journal of Communication Systems 2024 DOI: 10.1002/dac.5668
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• Smart Energy Borrowing and Relaying in Wireless-Powered Networks: A Deep Reinforcement Learning Approach
  Energies 2023 DOI: 10.3390/en16217433
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• Joint Optimization Framework for Minimization of Device Energy Consumption in Transmission Rate Constrained UAV-Assisted IoT Network
  IEEE Internet of Things Journal 2022 DOI: 10.1109/JIOT.2021.3128883
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• Maximization of instantaneous transmission rate in unmanned aerial vehicles‐supported self‐organized device‐to‐device network
  International Journal of Communication Systems 2022 DOI: 10.1002/dac.5064
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• Channel characterization and performance analysis of unmanned aerial vehicle‐operated communication system with multihop radio frequency–free‐space optical link in dynamic environment
  International Journal of Communication Systems 2020 DOI: 10.1002/dac.4568
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