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SNR Estimation Method based on SRS and DINet

Published: 17 August 2023 Publication History

Abstract

In conventional SNR estimation, the energy of the useful signal cannot be accurately calculated because of the noise in the received signal. At the same time, because of the random nature of noise, how to accurately estimate the noise is a common challenge in the engineering community. To address this problem, this paper proposes a signal-to-noise ratio (SNR) estimation method that combines the sounding reference signal (SRS) with the deep learning network DINet, where DINet is composed of denoising convolutional neural network (DnCNN) and image restoration convolutional neural network (IRCNN) in parallel. To demonstrate the higher estimation performance of our proposed method, we replicate some advanced algorithms, such as Boumard's algorithm, Qun X et al.'s improved algorithm, and M2M4 algorithm, and in the paper we refer to Boumard's algorithm and Qun X et al.'s improved algorithm as algorithm 1 and algorithm 2, respectively. At the transmitter side, to address the randomness of the noise distribution, we map the SRS into a nine-box grid on the resource block, and this arrangement facilitates a more accurate estimation of the noise and signal. At the receiver side, the SNR of each nine-box grid is calculated firstly using algorithm 1. Then the SNR is put into the corresponding resource unit and linear interpolation is performed on the resource block. Finally, the resource blocks are equated to images and input to DINet for denoising to obtain a more accurate value of the SNR estimate. Experiments show that the proposed method in this paper has a significant performance improvement compared with algorithm 2 and M2M4 algorithm.

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Cited By

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  • (2024)Performance and Optimization of Coding Techniques for Deep Space Communication Channels2024 IEEE 7th International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)10.1109/AUTEEE62881.2024.10869659(49-57)Online publication date: 27-Dec-2024

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ICCMS '23: Proceedings of the 2023 15th International Conference on Computer Modeling and Simulation
June 2023
293 pages
ISBN:9798400707919
DOI:10.1145/3608251
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 August 2023

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Author Tags

  1. DnCNN
  2. IRCNN
  3. SNR
  4. deep learning
  5. sounding reference signal (SRS)

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  • Research-article
  • Research
  • Refereed limited

Funding Sources

  • the Key Research and Development Project of Hainan Province
  • the National Natural Science Foundation of China

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ICCMS 2023

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  • (2024)Performance and Optimization of Coding Techniques for Deep Space Communication Channels2024 IEEE 7th International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)10.1109/AUTEEE62881.2024.10869659(49-57)Online publication date: 27-Dec-2024

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