Deep Receiver Design for Multi-carrier Waveforms Using CNNs

• URL: http://arxiv.org/abs/2006.02226v1
• Date: Tue, 2 Jun 2020 10:29:05 GMT
• Title: Deep Receiver Design for Multi-carrier Waveforms Using CNNs
• Authors: Yasin Yildirim, Sedat Ozer, Hakan Ali Cirpan
• Abstract summary: 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.
• Score: 8.9379057739817
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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