基于CNN深度学习的单载波和多载波信号分类

S. An, Mingyu Jang, D. Yoon
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引用次数: 1

摘要

在非合作环境中,为了从接收到的信号中恢复数据,接收端必须估计发送端使用的通信参数。本文提出了一种基于卷积神经网络深度学习的单载波和多载波信号分类算法,并分析了分类性能。仿真结果表明,该算法在加性高斯白噪声信道和梯度衰落信道中优于传统方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Classification of Single- and Multi-carrier Signals Using CNN Based Deep Learning
In a non-cooperative context, to recover data from the received signal, the receiver must estimate the communication parameters used in the transmitter. In this paper, we propose an algorithm for classifying single-carrier and multi-carrier signals by using convolutional neural network based deep learning and analyze classification performance. Simulation results show that the proposed algorithm outperforms the conventional methods in an additive white Gaussian noise channel and Rician fading channel.
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