Abstract
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.
Highlights
In the off-line training stage, a convolutional neural networks (CNNs) model is trained by the use of a large number of constellation diagrams labeled by the Signal-to-noise ratio (SNR) values
In order to demonstrate the impacts of modulation types on estimation accuracy, Figure 7 shows the mean squared error (MSE) of binary-phase shift keying (BPSK), quadrature-phase shift keying (QPSK), 8PSK, and 16PSK averaged in SNR under additive white Gaussian noise (AWGN) channel
Conclusion is paper proposes a deep learning (DL) based SNR estimation algorithm using constellation diagrams. e received signal is converted into a constellation diagram and recognized by AlexNet, InceptionV1, and VGG16 for SNR estimation
Summary
Signal-to-noise ratio (SNR), which defines the difference in level between the signal and the noise, is one of the most important parameters for spectrum management [1], channel resource allocation [2, 3], transmission power control [4], and adaptive modulation and coding [5]. In order to alleviate the impact of signal leakage that impairs the performance of SNR estimation, a weighting operator is suggested to predict and retrieve the leaked signal in [16]. These algorithms do not require pilot sequences, they usually suffer from significant estimation errors, especially when SNR is very low. In the off-line training stage, a CNN model is trained by the use of a large number of constellation diagrams labeled by the SNR values. In the online estimating stage, the received signal is preprocessed into a constellation diagram and fed into the trained CNN model, with which the SNR of the received signal is estimated.
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