An improved Graph-based SNR Estimation Algorithm

• Li Yang,
• Haoyu Wei,
• Guobing Hu,
• Wenqing Zhu,

Vol. 18, No. 10, October 31, 2024
10.3837/tiis.2024.10.013, Download Paper (Free):

• SNR estimation
• Signal to graph convertor
• Majorization order
• Order-preserving
• Normalized root mean square error
• Deflection coefficient

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.

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