An SNR Estimation Technique Based on Deep Learning

• Main Authors: Kai Yang, Zhitao Huang, Xiang Wang, Fenghua Wang
• Format: Article
• Language: English
• Published: MDPI AG 2019-10-01
• Series: Electronics
• Subjects: snr estimation; deep learning; m2m4 estimator; nda estimator; deep neural network
• Online Access: https://www.mdpi.com/2079-9292/8/10/1139

Signal-to-noise ratio (SNR) is a priori information necessary for many signal processing algorithms or techniques. However, there are many problems exsisting in conventional SNR estimation techniques, such as limited application range of modulation types, narrow effective estimation range of signal-to-noise ratio, and poor ability to accommodate non-zero timing offsets and frequency offsets. In this paper, an SNR estimation technique based on deep learning (DL) is proposed, which is a non-data-aid (NDA) technique. Second and forth moment (M2M4) estimator is used as a benchmark, and experimental results show that the performance and robustness of the proposed method are better, and the applied ranges of modulation types is wider. At the same time, the proposed method is not only applicable to the baseband signal and the incoherent signal, but can also estimate the SNR of the intermediate frequency signal.