Dr. Congcong Wang | Wireless Communication | Best Researcher Award

Dr. Congcong Wang | Wireless Communication | Best Researcher Award

Research Associate, Institute of Computing, Chinese Academy of Sciences, China

Congcong Wang is a dedicated Ph.D.-level Research Scientist currently serving as a Special Research Assistant at the Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China. She is recognized for her cutting-edge work in wireless communication systems, particularly in Visible Light Communication (VLC), Full-Spectrum Wireless Communication, and Body Area Networks. With over ten peer-reviewed publications in prestigious journals such as IEEE TCOM and Optical Express, and seven national and international patents, Dr. Wang combines theoretical insight with practical innovations in experimental platforms and system design.

Publication Profile

ORCID

🎓 Education Background

Congcong Wang has pursued advanced academic training leading to a doctoral-level research profile, focused intensively on next-generation wireless communication technologies. Her rigorous education laid a solid foundation in areas such as MIMO VLC systems, OFDM, and dimming control in communication systems.

💼 Professional Experience

Currently, Dr. Wang is affiliated with the Institute of Computing Technology, Chinese Academy of Sciences, where she works as a Special Research Assistant. In this role, she has contributed significantly to research and development efforts, combining theoretical modeling with real-world system implementations and contributing to international standards in wireless communication.

🏆 Awards and Honors

Congcong Wang has been recognized through numerous accolades, including accepted papers at prestigious IEEE conferences and journal publications. Her innovative work has led to multiple national and international patents, showcasing her as a leader in wireless communication R&D.

🔬 Research Focus

Her core research interests include MIMO-based Visible Light Communication, Full-Spectrum Wireless Communication systems, Robotic Body Area Wireless Networks, dynamic subcarrier activation schemes, and deep learning-based signal detection in OTFS systems. She is also exploring AI-enabled wireless sensing and channel prediction through advanced architectures like Sparse Graph Attention Networks and CanFormer.

📝 Conclusion

Congcong Wang stands out as a young, impactful researcher in the wireless communication domain. Her blend of theoretical rigor, experimental validation, and translational outcomes continues to influence next-generation communication technologies.

📚 Top Publications with Details

  1. Joint SIC-Based Precoding and Sub-connected Architecture Design for MIMO VLC SystemsIEEE Transactions on Communications, 2022

    • Cited by: 30+

    • DOI: 10.1109/TCOMM.2022.3142696

  2. Joint Ordered QR Precoding and SIC Detection for MIMO VLC SystemsOptics Express, 2023

    • Cited by: 10+

    • DOI: 10.1364/OE.471543

  3. A Dimmable OFDM Scheme With Dynamic Subcarrier Activation for VLCIEEE Photonics Journal, 2020

    • Cited by: 40+

    • DOI: 10.1109/JPHOT.2020.2964744

  4. A Generalized Dimming Control Scheme for Visible Light CommunicationsIEEE Transactions on Communications, 2021

    • Cited by: 60+

    • DOI: 10.1109/TCOMM.2020.3038665

  5. SIC-based Precoding Scheme with Sub-connected Architecture for MIMO VLC SystemsIEEE GLOBECOM, 2022

    • Cited by: 15+

    • DOI: 10.1109/GLOBECOM48099.2022.10003233

  6. Generalized Dimming Control Scheme with Optimal Dimming Control Pattern for VLCIEEE WCNC, 2020

    • Cited by: 25+

    • DOI: 10.1109/WCNC45663.2020.9118991

 

ELHADJ MOUSTAPHA DIALLO | wireless communication | Best Researcher Award

Mr. ELHADJ MOUSTAPHA DIALLO | wireless communication | Best Researcher Award

Ph.D student candidate, Chongqing university of posts and telecommunications, China

Elhadj Moustapha Diallo is a dedicated researcher and engineer specializing in Information and Communication Engineering 📡. With extensive experience in telecommunications, signal processing, and network optimization, he has contributed significantly to cutting-edge advancements in energy-efficient resource allocation, UAV-enabled networks, and deep learning applications in wireless systems. His expertise spans both academia and industry, having worked with top organizations such as Transsion, Huawei, and MTN. His research has been widely recognized, leading to multiple publications in prestigious IEEE conferences and journals 📖.

Publication Profile

🎓 Education

Elhadj Moustapha Diallo is currently pursuing his Ph.D. in Information and Communication Engineering at Chongqing University of Posts and Telecommunications, China (2021-2025) 🎓. He previously earned a Master’s degree in the same field (2018-2021) and holds a Bachelor’s degree in Telecommunications from the University Nongo Conakry, Guinea (2012-2021) 📡. His academic journey is marked by strong expertise in wireless communications, artificial intelligence applications, and optimization techniques in modern telecommunication systems.

💼 Experience

With a solid background in both research and industry, Diallo has served as an Image Evaluation Engineer at Transsion Company in China, where he specialized in image analysis and mobile camera evaluation 📷. His research work at the Chongqing Key Laboratory of Signal and Information Processing focused on 5G mobile communications, resource allocation, and IoT networks 🌍. Prior to that, he gained valuable experience in IT support, network engineering, and telecommunications at MTN, Huawei Technology Training Center, and Contact Center International 🔧.

🏆 Awards and Honors

Elhadj Moustapha Diallo has been recognized for his contributions to communication engineering, particularly in energy-efficient wireless networks 🏅. His research has been accepted at high-profile IEEE conferences, and he has actively collaborated with leading experts in the field. His achievements in optimizing telecommunication systems using deep learning and UAV-assisted networks have positioned him as an emerging expert in wireless technologies 🚀.

🔬 Research Focus

Diallo’s research interests include energy-efficient resource allocation, UAV-enabled networks, deep learning for wireless communications, and optimization techniques for 5G and beyond 📶. His studies explore novel approaches such as deep unfolding mechanisms, generative models for multi-carrier NOMA networks, and long-term energy consumption minimization for UAV-assisted content fetching. His innovative contributions are shaping the future of next-generation communication systems 🌎.

🔍 Conclusion

Elhadj Moustapha Diallo is a highly skilled researcher and telecommunications engineer whose contributions to energy-efficient wireless networks and advanced communication technologies have earned him recognition in academia and industry 🌟. His extensive research, publications, and practical experience make him a key innovator in the field of 5G, AI-driven optimization, and UAV-assisted networking. With a strong foundation in both theory and practice, Diallo continues to push the boundaries of next-generation communication systems 📡🚀.

📜 Publications

Energy Efficient Resource Allocation and Mode Selection for Content Fetching in Cellular D2D Networks (2021) – IEEE Wireless Telecommunications Symposium (WTS) 📡.

Content Fetching Delay Optimization-Based Caching and Resource Allocation for UAV-Enabled Networks (2024) – IEEE Access 🚀.

Optimizing Wireless Networks with Deep Unfolding: Comparative Study on Two Deep Unfolding Mechanisms (2024) – arXiv Preprint 📶.

Generative Model for Joint Resource Management in Multi-Cell Multi-Carrier NOMA Networks (2024) – Accepted at IEEE 10th International Conference on Computer and Communications (ICCC 2024) 📊.

OHDRL-Based Energy Consumption Optimization for Joint Content Fetching and Trajectory Design of UAVs (2024) – Accepted at IEEE 29th Asia Pacific Conference on Communications (APCC) 🚁.

Long-Term Energy Consumption-Minimization-based Joint Content Fetching and Trajectory Design of UAVs – Under review in Computer Communications 🛰️.