论文标题
使用深神经网络识别无线通道水龙头的数量
Identification of The Number of Wireless Channel Taps Using Deep Neural Networks
论文作者
论文摘要
在无线通信系统中，识别频道TAPS的数量提供了对通道脉冲响应（CIR）的增强估计。在这项工作中，已经通过深神经网络（DNN）实现了无线通道的数量的有效识别，在那里我们仅使用无线系统的传输和接收的信号来修改现有的DNN，并分析了其收敛性能。显示的结果表明，与现有的算法相比，所采用的DNN在识别频道TAPS的数量方面取得了卓越的性能，称为信号源的光谱加权识别（瑞士）。
In wireless communication systems, identifying the number of channel taps offers an enhanced estimation of the channel impulse response (CIR). In this work, efficient identification of the number of wireless channel taps has been achieved via deep neural networks (DNNs), where we modified an existing DNN and analyzed its convergence performance using only the transmitted and received signals of a wireless system. The displayed results demonstrate that the adopted DNN accomplishes superior performance in identifying the number of channel taps, as compared to an existing algorithm called Spectrum Weighted Identification of Signal Sources (SWISS).
