Nakagami-m Fading Channel Identification Using Adaptive Continuous Wavelet Transform and Convolutional Neural Networks

Channel identification is a useful function to support wireless telecommunication operations. The authors in this paper apply Convolutional Neural Networks (CNN) to the problem of channel identification, which is still an emerging research area. Because the digitized RF signal is a one-dimensional time series, different algorithms are applied to convert the time series to images using various Time Frequency Transform (TFT) including the CWTs, spectrogram, and Wigner Ville distribution. The approach is applied to a data set based on weather radar pulse signals generated in the laboratory of the author’s facilities on which different fading models are applied. These models are inspired by the tap-delay-line 3GPP configurations defined in the standards, but they have been customized with Nakagami-m fading distribution (3GPP-like fading models). The results show the superior performance of time–frequency CNN in comparison to 1D CNN for different values of Signal to Noise Ratio (SNR) in dB. In particular, the study shows that the Continuous Wavelet Transform (CWT) has the optimal performance in this data set, but the choice of the mother wavelet remains a problem to be solved (this is a well-known problem in the research literature). Then, this study also proposes an adaptive technique for the choice of the optimal mother wavelet, which is evaluated on the mentioned data set. The results show that the adaptive proposed approach is able to obtain the optimal performance for most of the SNR conditions.

BALDINI Gianmarco;  BONAVITACOLA Fausto; 

2023-06-09

MDPI OPEN ACCESS PUBLISHING

JRC129466

1999-4893 (online),   

https://www.mdpi.com/1999-4893/16/6/277,    https://publications.jrc.ec.europa.eu/repository/handle/JRC129466,   

10.3390/a16060277 (online),