Multicarrier Waveforms Classification with LDA and CNN for 5G

Accurate classification of multi-carrier waveforms is significant for ensuring quality signal reception, improved system throughput, and reduce the power consumption in future wireless generations as 5G. The aim of this paper is to improve the precision classification of various multicarrier waveforms. Here, we propose a novel representation of multicarrier signals in AWGN environment and use suitable networks for classification, which utilizes deep convolutional neural networks to classify OFDM-QAM, and FBMC-OQAM. LDA-based (Linear Discriminant Analysis) method is proposed in this paper to reduce the input dimensions of CNN. The results reveal that LDA-CNN is a promising candidate for wireless communication.