About | Contact Us | Register | Login
ProceedingsSeriesJournalsSearchEAI
Communications and Networking. 15th EAI International Conference, ChinaCom 2020, Shanghai, China, November 20-21, 2020, Proceedings

Research Article

Channel Estimation Algorithm Based on Demodulation Reference Signal in 5G

Download(Requires a free EAI acccount)
16 downloads
Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-030-67720-6_25,
        author={Bingguang Deng and Xiaofang Min and Siyi Yu and Qianqian Ye},
        title={Channel Estimation Algorithm Based on Demodulation Reference Signal in 5G},
        proceedings={Communications and Networking. 15th EAI International Conference, ChinaCom 2020, Shanghai, China, November 20-21, 2020,  Proceedings},
        proceedings_a={CHINACOM},
        year={2021},
        month={2},
        keywords={5G Linear minimum mean square error SNR Time delay Channel estimation},
        doi={10.1007/978-3-030-67720-6_25}
    }
    
  • Bingguang Deng
    Xiaofang Min
    Siyi Yu
    Qianqian Ye
    Year: 2021
    Channel Estimation Algorithm Based on Demodulation Reference Signal in 5G
    CHINACOM
    Springer
    DOI: 10.1007/978-3-030-67720-6_25
Bingguang Deng1, Xiaofang Min1,*, Siyi Yu1, Qianqian Ye1
  • 1: School of Communication and Information Engineering
*Contact email: S180101183@stu.cqupt.edu.cn

Abstract

In order to track 5G downlink shared channel in real time and meanwhile to reduce computational complexity, a linear minimum mean square error algorithm based on demodulation reference signal adaptive parameter estimation is proposed. Firstly, the SNR nonlinear centralized optimization problem is transformed into a multivariable linear programming problem due to the restriction of non-uniform energy distribution in time-domain channel. Secondly, considering the uncertainty of multipath delay channel, the combination of negative exponential distribution model and generalized correlation algorithm is taken advantage of so that the original problem is turned into a specific parameter optimization problem. At the same time, according to the obtained delay parameters and SNR, the most appropriate interpolation coefficient is selected for the LMMSE channel estimation by combining with the sliding window, which avoids the matrix inversion process, realizes the real-time matching of parameters, and reduces the computational complexity. The simulation results show that the proposed algorithm has better system performance compared with the classical channel estimation algorithm.

Keywords
5G, Linear minimum mean square error, SNR, Time delay, Channel estimation
Published
2021-02-02
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-030-67720-6_25
Copyright © 2020–2025 ICST
EBSCOProQuestDBLPDOAJPortico
EAI Logo

About EAI

  • Who We Are
  • Leadership
  • Research Areas
  • Partners
  • Media Center

Community

  • Membership
  • Conference
  • Recognition
  • Sponsor Us

Publish with EAI

  • Publishing
  • Journals
  • Proceedings
  • Books
  • EUDL