Deep Learning Based Hybrid Beamforming for mmWave Dual-Functional Radar-Communication

Authors

  • Xiaoyou Yu Hunan University College of Computer Science and Electronic Engineering https://orcid.org/0000-0001-8123-8296
  • Tianchu Li Hunan University, China
  • Ziyun Tian Hunan University College of Computer Science and Electronic Engineering
  • Miao Yu Hunan University College of Computer Science and Electronic Engineering

Abstract

We propose a novel deep learning (DL) based HBF design for the dual-functional radar-communication (DFRC) system with the millimeter wave (mmWave) massive multiple-in-multiple-output (MIMO) architecture, in which the HBF is formulated as a non-convex optimization problem. First, the DL-based HBF is designed to minimize the sum-MSE of downlink communications while carrying out necessary radar sensing concurrently. Then the synchronization noise is attached to the input channel data to enhance the robustness of the CNN. After that, an attention mechanism is added into the prediction stage to improve the prediction without affecting the accuracy of the prediction results. Finally, the numerical simulation results show significant tradeoff performance improvements between communication and radar sensing can be obtained over existing HBF designs.

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Author Biographies

Xiaoyou Yu, Hunan University College of Computer Science and Electronic Engineering

Xiaoyou Yu (M'21) received the M.S. and Ph.D. degrees in information and communication engineering from National University of Defense Technology, China, in 1995 and 1998, respectively. He is an A./Prof. at the College of Information Science and Electronic Engineering, Hunan University, China, and a part-time Research Fellow at Advanced Technology Institute. His research interests include array signal processing, dual-function radar-communication, radar and communication coexistence, integrated sensing and communications, anti-jamming, mobile computing, Internet of Vehicles and intelligent transportation systems, artificial intelligence, and its application to signal processing.

Tianchu Li, Hunan University, China

Tianchu Li was born in Changsha, Hunan, China, in 1999. He received a bachelor's degree in communication engineering from Hunan University, Changsha, China, in 2021. He is currently pursuing an M.S. degree at the College of Computer Science and Electronic Engineering, Hunan University. Her research interests include radar communication integration systems.

Ziyun Tian, Hunan University College of Computer Science and Electronic Engineering

Ziyun Tian was born in Changsha, Hunan, China, in 1996. She received her bachelor's degree in Internet of Things engineering from Jiangxi University of Finance and Economics, Nanchang, China, in 2021. She is currently pursuing an M.S. degree in the College of Computer Science and Electronic Engineering at Hunan University. Her research interests include integrated sensing and communication systems.

Miao Yu, Hunan University College of Computer Science and Electronic Engineering

Miao Yu was born in Changsha, Hunan, China, in 1996. She received her bachelor's degree in communication engineering from Hunan Normal University, Changsha, China, in 2018. She is pursuing an M.S. degree in the College of Computer Science and Electronic Engineering, at Hunan University. Her research interests include radar communication integration systems.

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Published

2024-09-29

How to Cite

Yu, X., Li, T., Tian, Z., & Yu, M. (2024). Deep Learning Based Hybrid Beamforming for mmWave Dual-Functional Radar-Communication. IEEE Latin America Transactions, 22(10), 871–880. Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/8910

Issue

Vol. 22 No. 10 (2024): Ordinary Issue

Section

Electronics

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