RK
Dr. Ramandeep Kaur ECE Punjabi University Patiala
Punjabi University, Thapar Institute of Engineering and Technology, Punjab Technical University
0000-0002-6930-7348
About
I am an Associate Professor at Punjabi University, Patiala, working in Optical Communication, Wireless Optical Communication, Artificial Intelligence and Machine Learning. My research includes Passive Optical Networks, FSO, Inter-Satellite and Underwater Optical Wireless Communication. I am currently working on a ₹73.81 lakh RUSA-funded project and developing new research projects in emerging areas of communication and AI. My research contributions have been recognized with a Travel and Participation Award by DST and the Editor of Distinction Award 2026 from Springer Nature/Scientific Reports.
Employment
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Punjabi University Associate Professor2011 - Present
Education
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Punjabi University PhD2016 - 2021
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Thapar Institute of Engineering and Technology M.E (ECE)2009 - 2011
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Punjab Technical University B.Tech2005 - 2009
Projects & Funding
Projects & funding information is unavailable.
Publications (43)
- Formation and performance analysis of a 2 µm region thulium-doped fiber laser using different pump wavelengths Save
- Performance evaluation of zero cross correlation code (ZCC code) based passive optical network Save
- Machine Learning based modulation format classification framework for inter-satellite optical wireless communication system (IsOWCS) Save
- Predictive modeling for VLC systems: an artificial neural networks approach to real-world performance Save
- Leveraging machine learning for space division multiplexing performance: a classifier-focused evaluation Save
- Performance prediction of visible light communication system using machine learning: a comparative analysis of classifiers Save
- Assessing the impact of geometric losses on WDM-Based underwater optical wireless communication system Save
- Effects of thulium (Tm) concentration and numerical aperture on the gain of thulium doped fiber amplifier (TDFA) for amplification in 2 µm region Save
- Predicting the performance of broadband passive optical networks using machine learning Save
- Artificial neural networks as predictive tools for space division multiplexing system performance Save