Deep Receiver Design for Multi-carrier Waveforms Using CNNs
This work addresses receiver design for current and next-generation wireless communication systems, but it appears incremental as it applies a known deep learning method to a specific domain.
The authors tackled the problem of signal detection and demodulation in multi-carrier wireless systems by proposing a CNN-based receiver, which outperformed classical methods in simulations for waveforms like OFDM and GFDM.
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.