KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
권호 표지
DOI QR코드

DOI QR Code

An improved Graph-based SNR Estimation Algorithm

  • Li Yang (School of Electronic and Information Engineering, Jinling Institute of Technology) ;
  • Haoyu Wei (School of Electronic and Information Engineering, Jinling Institute of Technology) ;
  • Guobing Hu (School of Electronic and Information Engineering, Jinling Institute of Technology) ;
  • Wenqing Zhu (School of Information Science and Technology, Artificial Intelligence, Nanjing Forestry University)
  • Received : 2023.09.04
  • Accepted : 2024.10.09
  • Published : 2024.10.31
Download PDF
⟨ Previous Next ⟩

Abstract

The previous graph-based estimation algorithm is of poor performance in low signal-to-noise ratio (SNR) and is failure for frequency band signals. An improved graph-based SNR estimator using blocking sum of spectrum of the observed signal is proposed in this article, which consists of two stages: fitting the SNR estimation expression by training samples and estimating the SNR of the test signal. In the former stage, the training samples are firstly segmented with overlap, then the real part of the spectrum of each segment is blocked without overlap and summed to be transformed to a graph, and accordingly the average degree sum (DS) of the graphs is calculated. Afterwards, a nonlinear fitting of the relationship between the average DS and the SNR is obtained using a trust region fitting algorithm. In the latter stage, the average DS of the test signal is obtained by applying the mentioned scheme. Subsequently, substitute it into the fitted expression to estimate the SNR. Moreover, we analyze the impact mechanism of the order preserving between the majorization order of input samples and the majorization order of vertex probability vectors, which providing a basis for the interpretability of graph-based SNR estimator and for the selection of input forms for graph transform in the estimation. Simulation results demonstrate that the proposed algorithm has a superiority performance for both baseband and frequency band signals under low SNR and multipath or fading channels, with a computational complexity of approximately 50% compared to the existing graph-based algorithm.

Keywords

Acknowledgement

This study was financially supported by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (Project No. 20KJA510008), and Training Program of Innovation and Entrepreneurship for Undergraduates (Project No. 202313573083Y).

References

  1. D.R. Pauluzzi and N.C. Beaulieu, "A comparison of SNR estimation techniques for the AWGN channel," IEEE Transactions on Communications, vol.48, no.10, pp.1681-1691, 2000. https://doi.org/10.1109/26.871393
  2. R. Tandra and A. Sahai, "SNR Walls for Signal Detection," IEEE Journal of selected topics in Signal Processing, vol.2, no.1, pp.4-17, 2008. https://doi.org/10.1109/JSTSP.2007.914879
  3. T. L. Marzetta, "Noncooperative Cellular Wireless with Unlimited Numbers of Base Station Antennas," IEEE transactions on wireless communications, vol.9, no.11, pp.3590-3600, 2010. https://doi.org/10.1109/TWC.2010.092810.091092
  4. X. Yi and C. Zhong, "Deep Learning for Joint Channel Estimation and Signal Detection in OFDM Systems," IEEE Communications Letters, vol.24, no.12, pp.2780-2784, 2020. https://doi.org/10.1109/LCOMM.2020.3014382
  5. H. S. Liao, L. Gan, and P. Wei, "A blind SNR estimation method for radar signals," in Proc. of 2009 IET International Radar Conference, pp.1-4, 2009.
  6. N. Wu, H. Wang, and J.-M. Kuang, "Maximum likelihood signal-to-noise ratio estimation for coded linearly modulated signals," IET Communications, vol.4, no.3, pp.265-271, 2010. https://doi.org/10.1049/iet-com.2009.0272
  7. G. Romano, "On Asymptotic Efficiency of the M2M4 Signal-to-Noise Estimator for Deterministic Complex Sinusoids," Sensors, vol.21, no.15, 2021.
  8. X. Zhang and Z. Dong, "Research on SVD SNR Estimation Algorithm," in Proc. of 2022 12th International Conference on Information Technology in Medicine and Education (ITME), pp.569-574, 2022.
  9. S. Barbarossa and S. Sardellitti, "Topological Signal Processing: Making Sense of Data Building on Multiway Relations," IEEE Signal Processing Magazine, vol.37, no.6, pp.174-183, 2020. https://doi.org/10.1109/MSP.2020.3014067
  10. H. Li, D. Wang, X. Zhang, and G. Gao, "Frame-Level Signal-to-Noise Ratio Estimation Using Deep Learning," in Proc. of Interspeech 2020, pp.4626-4630, 2020.
  11. K. Yan, H.-C. Wu, and X. Huang, "Blind SINR Estimation Based on Graph Sparsity," in Proc. of GLOBECOM 2020 -2020 IEEE Global Communications Conference, pp.1-7, 2020.
  12. K. Yan, H.-C. Wu, H. Xiao, and X. Zhang, "Novel Robust Band-Limited Signal Detection Approach Using Graphs," IEEE Communications Letters, vol.21, no.1, pp.20-23, 2017. https://doi.org/10.1109/LCOMM.2016.2618871
  13. K. Yan, H.-C. Wu, C. Busch, and X. Zhang, "Graph representation of random signal and its application for sparse signal detection," Digital Signal Processing, vol.96, 2020.
  14. Y. A. Eldemerdash, O. A. Dobre, O. ureten, and T. Yensen, "A Robust Modulation Classification Method for PSK Signals Using Random Graphs," IEEE Transactions on Instrumentation and Measurement, vol.68, no.2, pp.642-644, 2019. https://doi.org/10.1109/TIM.2018.2849478
  15. L. Pu, H.-C. Wu, K. Yan, Z. Gao, X. Wang, and W. Xiang, "Novel Three-Hierarchy MultipleTag-Recognition Technique for Next Generation RFID Systems," IEEE Transactions on Wireless Communications, vol.19, no.2, pp.1237-1249, 2020. https://doi.org/10.1109/TWC.2019.2952110
  16. X. Yan, G. Liu, H.-C. Wu, G. Zhang, Q. Wang, and Y. Wu, "Robust Modulation Classification Over α-Stable Noise Using Graph-Based Fractional Lower-Order Cyclic Spectrum Analysis," IEEE Transactions on Vehicular Technology, vol.69, no.3, pp.2836-2849, 2020. https://doi.org/10.1109/TVT.2020.2965137
  17. G. Hu, Z. Chen, P. Zhao, and L. Yang, "Graph-based confidence verification for BPSK signal analysis under low SNRs," Signal Processing, vol.206, 2023.
  18. L. Yang, G. Hu, X. Xu, and P. Zhao, "Modulation Recognition of BPSK/QPSK Signals based on Features in the Graph Domain," KSII Transactions on Internet and Information Systems, vol.16, no.11, pp.3761-3779, 2022.
  19. R. J. Hickey, "Continuous majorisation and randomness," Journal of Applied Probability, vol.21, no.4, pp.924-929, 1984. https://doi.org/10.2307/3213709
  20. B. Widrow, I. Kollar, and M.-C. Liu, "Statistical theory of quantization," IEEE Transactions on Instrumentation and Measurement, vol.45, no.2, pp.353-361, 1996. https://doi.org/10.1109/19.492748
  21. G. Marsaglia and W. W. Tsang, "A Fast, Easily Implemented Method for Sampling from Decreasing or Symmetric Unimodal Density Functions," SIAM Journal on Scientific and Statistical Computing, vol.5, no.2, pp.349-359, 1984. https://doi.org/10.1137/0905026
  22. N. M. M. de Abreu, "Old and new results on algebraic connectivity of graphs," Linear Algebra and its Applications, vol.423, no.1, pp.53-73, 2007. https://doi.org/10.1016/j.laa.2006.08.017
  23. S. M. Kay, Fundamentals of statistical signal processing: estimation theory, Prentice-Hall, Inc., 1993.
  24. J. Nocedal, S. J. Wright, Numerical Optimization, Springer Series in Operations Research and Financial Engineering, Springer, 1999.