Signal-to-noise ratio (SNR) estimation is a fundamental task of spectrum management and data transmission. Existing methods for SNR estimation usually suffer from significant estimation errors when SNR is low. This paper proposes a deep learning (DL) based SNR estimation algorithm using constellation diagrams. Since the constellation diagrams exhibit different patterns at different SNRs, the proposed algorithm achieves SNR estimation via constellation diagram recognition, which can be easily handled based on DL. Three DL networks, AlexNet, InceptionV1, and VGG16, are utilized for DL based SNR estimation. Experimental results show that the proposed algorithm always performs well, especially in low SNR scenarios.

中文翻译：

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基于星座图的基于深度学习的信噪比估计

信噪比（SNR）估计是频谱管理和数据传输的基本任务。当SNR低时，现有的SNR估计方法通常会遭受很大的估计误差。本文提出了一种使用星座图的基于深度学习（SNR）的SNR估计算法。由于星座图在不同的信噪比下显示出不同的模式，因此该算法通过星座图识别实现了SNR估计，该算法可以轻松地基于DL进行处理。三个DL网络AlexNet，InceptionV1和VGG16用于基于DL的SNR估计。实验结果表明，所提出的算法始终表现良好，特别是在低信噪比的情况下。