Ph.D. IIT (ISM), Dhanbad India
Strengths for the Award
Innovative Research Focus: Chandan Kumar’s research in signal detection and channel estimation for OTFS systems, particularly using deep learning techniques, demonstrates a strong alignment with cutting-edge technology and advancements in communication systems.
Notable Publications: His recent publication on low-complexity stationary iteration-based approximate inversion for signal detection in OTFS systems in the prestigious journal Physical Communication highlights the significance and quality of his contributions.
Collaborative Efforts: Collaborations with esteemed researchers from IISc Bangalore and SR University reflect his ability to work effectively within the academic community and contribute to significant research initiatives.
Diverse Expertise: His background in Electronics and Communication Engineering, combined with advanced skills in Mathematics and Computing, positions him well for interdisciplinary research applications.
Areas for Improvement
Broader Research Impact: While his focus on OTFS and deep learning is highly specialized, further expansion into other areas of signal processing or communication systems could enhance his research impact.
Citation Index and Patents: Increasing the citation index and pursuing additional patents could strengthen his profile and recognition within the research community.
Editorial Appointments: Gaining editorial roles or contributing to review boards could enhance his visibility and influence in the field.
Conclusion
Chandan Kumar’s innovative approach to signal detection and channel estimation using advanced methods and his notable publications make him a strong candidate for the Best Researcher Award. His research contributes significantly to the advancement of communication systems, particularly in the context of 5G and future technologies. Addressing areas for improvement, such as expanding his research scope and increasing citations, could further strengthen his candidacy. Overall, his contributions and ongoing work reflect a promising and impactful research career.
Chandan Kumar is a dedicated researcher currently pursuing a Ph.D. at the Indian Institute of Technology (Indian School of Mines), Dhanbad, focusing on signal detection and channel estimation in OTFS systems using deep learning (DL) and conventional methods. With a strong academic foundation in Electronics and Communication Engineering (B.Tech., DIT Dehradun) and Mathematics and Computing (M.Tech., IIT Patna), he brings a multidisciplinary approach to his research. Chandan’s work is aimed at advancing 5G and 6G communication technologies.
Profile
Google Scholar
Education 🎓
Chandan Kumar holds a B.Tech. in Electronics and Communication Engineering from DIT Dehradun and an M.Tech. in Mathematics and Computing from IIT Patna. He is currently pursuing a Ph.D. in Electronics and Communication Engineering at IIT (ISM) Dhanbad, with research focused on signal detection and channel estimation for OTFS systems.
Experience 💼
Chandan has extensive experience in research, particularly in signal processing and wireless communications. His expertise spans low-complexity detection methods, deep learning techniques, and 5G/6G technologies. He has collaborated with leading academics and researchers, contributing to cutting-edge projects in the field.
Research Interest 🔍
Chandan’s research interests center around signal detection and channel estimation for OTFS systems, leveraging both conventional and machine learning/deep learning techniques. His work primarily focuses on 5G and 6G communication systems, exploring methods like MMSE and MP detectors, and developing neural networks for MIMO-OTFS environments.
Awards & Recognitions 🏆
Chandan Kumar’s innovative contributions have been recognized by various institutions. He has received accolades for his research work, including collaborations with esteemed professors from IISc Bangalore and SR University Warangal, further establishing his impact in the field.
Publications 📚
- Kumar, C., Mitra, D., & Mishra, H.B. (2024). “Low complexity stationary iteration-based approximate inversion for signal detection in OTFS system,” Physical Communication, 2024, 102469. Cited by 5 articles.