Deep Receiver Design for Multi-carrier Waveforms Using CNNs
Signal Processing
2020-06-04 v1 Machine Learning
Systems and Control
Systems and Control
Abstract
In this paper, a deep learning based receiver is proposed for a collection of multi-carrier wave-forms including both current and next-generation wireless communication systems. In particular, we propose to use a convolutional neural network (CNN) for jointly detection and demodulation of the received signal at the receiver in wireless environments. We compare our proposed architecture to the classical methods and demonstrate that our proposed CNN-based architecture can perform better on different multi-carrier forms including OFDM and GFDM in various simulations. Furthermore, we compare the total number of required parameters for each network for memory requirements.
Keywords
Cite
@article{arxiv.2006.02226,
title = {Deep Receiver Design for Multi-carrier Waveforms Using CNNs},
author = {Yasin Yildirim and Sedat Ozer and Hakan Ali Cirpan},
journal= {arXiv preprint arXiv:2006.02226},
year = {2020}
}
Comments
PrePrint for TSP Conference