Dr. Xiaofeng Liu | Wireless Communication | Best Researcher Award

Published on by Computer Scientists Awards

Dr. Xiaofeng Liu | Wireless Communication | Best Researcher Award

Dr. Xiaofeng Liu | Lecture | Yancheng Teachers University | China

Dr. Xiaofeng Liu is a dedicated researcher and lecturer in Artificial Intelligence with a strong background in wireless communications, machine learning, and statistical inference. His research primarily focuses on developing advanced algorithms for massive MIMO systems, channel estimation, and machine learning-driven communication models. Dr. Liu has significantly contributed to the integration of statistical learning frameworks in communication system design, particularly through innovations like correlated hybrid message passing and generative diffusion models for channel estimation. His collaborative work with experts from leading research laboratories has produced high-impact publications in IEEE journals, reflecting both theoretical advancement and practical application in intelligent communication systems. His inventive contributions are further evident in several granted Chinese invention patents related to MIMO positioning, channel modeling, and beamspace communications. Dr. Liu’s research achievements are widely recognized, with his publications indexed in Scopus and Google Scholar, accumulating over 135 citations, an h-index of 6, and an i10-index of 5. His scholarly record demonstrates consistent contributions to next-generation wireless communication technologies, bridging the gap between deep learning models and complex signal processing challenges.

Publication Profile

Google Scholar | ORCID

Featured Publications 

Liu, X., Gong, X., & Fu, X. (2025). Activity detection and channel estimation based on correlated hybrid message passing for grant-free massive random access. Entropy.

Fu, X., Gong, X., Liu, X., Sun, R., Shen, Q., & Gao, X. (2025). Beamspace multi-ACB for mMTC in massive MIMO system. IEEE Transactions on Vehicular Technology.

Gong, X., Liu, X., Lu, A. A., Gao, X., Xia, X. G., Wang, C. X., & You, X. (2025). Digital twin of channel: Diffusion model for sensing-assisted statistical channel state information generation. IEEE Transactions on Wireless Communications.

Gong, X., Lu, A. A., Fu, X., Liu, X., Gao, X., & Xia, X. G. (2023). Semisupervised representation contrastive learning for massive MIMO fingerprint positioning. IEEE Internet of Things Journal.

Liu, X., Wang, W., Gong, X., Fu, X., Gao, X., & Xia, X. G. (2023). Structured hybrid message passing based channel estimation for massive MIMO-OFDM systems. IEEE Transactions on Vehicular Technology.

ELHADJ MOUSTAPHA DIALLO | wireless communication | Best Researcher Award

Published on by Computer Scientists Awards

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 🛰️.