Joint Communications and Sensing Hybrid Beamforming Design via Deep Unfolding
About this Topic:
Joint communications and sensing is expected to be a key capability of future wireless networks, enabling communication and environmental awareness within a unified system. In massive multiple antenna systems, hybrid beamforming offers an effective balance between beamforming performance, hardware cost, and power consumption. However, hybrid beamforming design for joint communications and sensing is challenging due to the coupling between analog and digital precoders and the need to balance communication throughput with sensing accuracy.
This webinar presents a fast hybrid beamforming design based on deep unfolding. The approach first examines the structure of the joint optimization problem and identifies key differences in the roles of the analog and digital precoders. Based on this insight, a modified projected gradient ascent method is introduced to improve convergence efficiency. The method is then extended using a deep unfolded framework, where learnable parameters further accelerate convergence while maintaining the interpretability and flexibility of the original algorithm. Simulation results show notable improvements in communication performance and sensing accuracy compared with conventional designs, along with a significant reduction in computational complexity and run time. The webinar demonstrates how model-driven learning can enable efficient and scalable joint communications and sensing solutions.
About the Presenter:
Nhan Thanh Nguyen received the B.S. degree in electronics and telecommunications engineering from the Hanoi University of Science and Technology, Hanoi, Vietnam, in 2014, and the M.S. and Ph.D. degrees in electrical and information engineering from the Seoul National University of Science and Technology, Seoul, South Korea, in 2017 and 2020, respectively.
He is currently an Assistant Professor at the Centre for Wireless Communications, University of Oulu, Finland, where he worked as a Postdoctoral Researcher from September 2020 to January 2023. His research interests include signal processing, optimization, and applied machine learning for wireless communications and sensing.
Dr. Nguyen was the recipient of the Best M.S. Thesis Award (2017), Best Ph.D. Dissertation Award (2020), Best Paper Awards at the ISAP 2025, IEEE SPAWC 2023, IEEE SSP 2023, and ATC 2021. He was recognized by the IEEE Communications Society as an Exemplary Editor for his editorial contributions to IEEE Wireless Communications Letters.
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