Deep Receiver Design for Multi-carrier Waveforms Using CNNs

TL;DR

This paper introduces a CNN-based receiver for multi-carrier wireless signals, demonstrating improved detection and demodulation performance over classical methods across various waveform types.

Contribution

The paper presents a novel deep learning architecture that jointly detects and demodulates multi-carrier signals, outperforming traditional approaches in accuracy and efficiency.

Findings

CNN-based receiver outperforms classical methods in simulations

The proposed architecture handles multiple waveform types effectively

Reduced memory requirements compared to other neural network models

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.