论文标题
使用CNN的多载波波形的深度接收器设计
Deep Receiver Design for Multi-carrier Waveforms Using CNNs
论文作者
论文摘要
在本文中，提出了一个基于深度学习的接收器，用于包括当前和下一代无线通信系统在内的多载波波形组合。特别是，我们建议使用卷积神经网络（CNN）共同检测和解调无线环境中接收器的接收信号。我们将我们提出的体系结构与经典方法进行比较，并证明我们提出的基于CNN的体系结构可以在不同的多载波形式上更好地在各种模拟中进行OFDM和GFDM。此外，我们比较了每个网络的所需参数总数，以确保内存要求。
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.
