TrsNet: A TRS-based deep learning network for carrier frequency offset estimation in 5G system

Xiaolei Li (), Yubo Wang, Xu Zhao, Kunpeng Xu, Hongguang Dai, Qian Zhang, Yubing Zhang and Jing Wang
Additional contact information
Xiaolei Li: Beijing Smart-Chip Microelectronics Technology Co., Ltd.
Yubo Wang: Beijing Smart-Chip Microelectronics Technology Co., Ltd.
Xu Zhao: Beijing Smart-Chip Microelectronics Technology Co., Ltd.
Kunpeng Xu: Beijing Smart-Chip Microelectronics Technology Co., Ltd.
Hongguang Dai: Beijing Smart-Chip Microelectronics Technology Co., Ltd.
Qian Zhang: Beijing Smart-Chip Microelectronics Technology Co., Ltd.
Yubing Zhang: Beijing Smart-Chip Microelectronics Technology Co., Ltd.
Jing Wang: Beijing Smart-Chip Microelectronics Technology Co., Ltd.

Telecommunication Systems: Modelling, Analysis, Design and Management, 2025, vol. 88, issue 1, No 2, 17 pages

Abstract: Abstract This article proposes a deep learning network, TrsNet, based on Tracking Reference Signal (TRS) for carrier frequency offset (CFO) estimation in 5G systems. Due to the use of Orthogonal Frequency Division Multiplexing technology in the 5G downlink, the system is susceptible to CFO, which can lead to signal amplitude attenuation and phase distortion, thereby affecting communication performance. To address the issues above, we propose designing and implementing a deep learning CFO estimation network, TrsNet, based on TRS, which improves the accuracy and robustness of CFO estimation by learning critical features of TRS signals. Through simulation experiments under different signal-to-noise ratios and CFO conditions, the performance of TrsNet in AWGN channels was verified. The results showed that TrsNet has a strong anti-noise interference ability, which can solve the limitations of traditional algorithms in estimation accuracy and range. At the same time, compared with similar deep learning methods, the proposed network model has lower complexity and more robust adaptability. Finally, this article also explores the challenges of applying deep learning technology in 5G communication and provides prospects for future research directions.

Keywords: 5G; OFDM; Deep learning; TRS; CFO (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11235-024-01231-5 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:telsys:v:88:y:2025:i:1:d:10.1007_s11235-024-01231-5

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/11235

DOI: 10.1007/s11235-024-01231-5

Access Statistics for this article

Telecommunication Systems: Modelling, Analysis, Design and Management is currently edited by Muhammad Khan

More articles in Telecommunication Systems: Modelling, Analysis, Design and Management from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().