Estimation of Signal-to-Noise Ratio Based on Feature Selection and Weight Optimization of High-Order Moments

被引：0
|
作者：
Li, Tao [1 ,2 ]
Su, Nan [3 ]
Wei, Di-Shan [3 ]
Li, Yong-Zhao [2 ]
Zhu, Ruo-Nan [2 ]
Zhao, Xin-Yu [2 ]
Zhou, Shuai [2 ]
机构：
[1] Guangzhou Institute of Technology, Xidian University, Guangdong, Guangzhou
[2] School of Telecommunication Engineering, Xidian University, Shaanxi, Xi'an
[3] Beijing Institute of Tracking and Telecommunications Technology, Beijing
来源：
Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2024年 / 52卷 / 12期
关键词：
estimation of signal-to-noise ratio; feature selection; high-order moment; Nakagami-fading channel; op⁃timization of weights;
D O I：
10.12263/DZXB.20230993
中图分类号：
学科分类号：
摘要：
As an important part of signal parameter estimation, signal-to-noise ratio (SNR) estimation can provide pri- or information for power control, modulation classification, channel estimation, and dynamic mode switching, etc. Recently, high-order moments (HOMs) based algorithms have been widely concerned due to the advantages of low computational complexity and high real-time property. However, the estimation performance of the HOMs-based algorithms is still constrained in extremely low or high SNR regions. In this paper, a SNR estimation algorithm based on feature selection and linear combination of HOMs is designed, according to the distribution characteristics of HOMs. Firstly, the HOMs are screened by analyzing the relationship between different moments and SNR values. Based on this, we resort to the linear combination of the selected HOMs to estimate SNR. And the weights of linear combination are calculated by designing an optimization problem. The simulation results show that the proposed SNR estimation scheme makes a tradeoff among the estimation performance of high and low SNR regions. Compared with the existing HOMs-based algorithms, the proposed algorithm has a more comprehensive performance in the range of -10 dB to 20dB. © 2024 Chinese Institute of Electronics. All rights reserved.
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页码：3976 / 3984
页数：8
相关论文
共 23 条
  • [1] PAULUZZI D R, BEAULIEU N C., A comparison of SNR estimation techniques for the AWGN channel, IEEE Transactions on Communications, 48, 10, pp. 1681-1691, (2000)
  • [2] WIESEL A, GOLDBERG J, MESSER H., Non-data-aided signal-to-noise-ratio estimation, 2002 IEEE International Conference on Communications (ICC), pp. 197-201, (2002)
  • [3] LOPEZ-VALCARCE R, MOSQUERA C, GAPPMAIR W., Iterative envelope-based SNR estimation for nonconstant modulus constellations, 2007 IEEE 8th Workshop on Signal Processing Advances in Wireless Communications, pp. 1-5, (2007)
  • [4] DAS A., NDA SNR estimation: CRLBs and EM based estimators, 2008 IEEE Region 10 Conference, pp. 1-6, (2008)
  • [5] GAPPMAIR W, LOPEZ-VALCARCE R, MOSQUERA C., Cramer-Rao lower bound and EM algorithm for envelope-based SNR estimation of nonconstant modulus constellations, IEEE Transactions on Communications, 57, 6, pp. 1622-1627, (2009)
  • [6] AHMAD RAZA M, HUSSAIN A., Cramer-Rao bound for SNR estimation of hyper-cubic signals over Gaussian channel, IEEE Communications Letters, 18, 12, pp. 2097-2100, (2014)
  • [7] AHMAD RAZA M, HUSSAIN A., Maximum likelihood SNR estimation of hyper cubic signals over Gaussian channel, IEEE Communications Letters, 20, 1, pp. 45-48, (2016)
  • [8] CHEN C F, WU J Y, WANG C H, Et al., On a new SNR estimation approach with Polar codes, 2021 IEEE 4th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), pp. 727-732, (2021)
  • [9] DAN S, GE L D., On the blind SNR estimation for IF signals, 2006 First International Conference on Innovative Computing, Information and Control-Volume I, pp. 374-378, (2006)
  • [10] SUI D, GE L D., A blind SNR estimator for digital bandpass signals, Journal of Electronics (China), 25, 1, pp. 7-13, (2008)
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