摘要：Due to the intrinsic imaginary interference among subcarriers, the channel estimation problem has become one of the main difficulties of the filter bank multicarrier (FBMC) systems. In this paper, we propose a novel channel estimation scheme based on residual networks (ResNet)-deep neural networks (DNN), called as Res-DNN scheme, for the FBMC systems. In the Res-DNN scheme, the conventional channel estimation and equalization module and the demapping module are replaced by a Res-DNN model of deep learning. Simulation results show that the channel estimation performance of the Res-DNN scheme is greatly superior to other schemes in terms of bit error rate (BER).

关键字：FBMC OQAM channel estimation deep neural networks deep learning residual networks

ISSN号：9781424456680

发表日期：2018-01-01

期刊分区（SCI为中科院分区）：无

收录情况：EI（工程索引）,CPCI-S（科技会议录索引）

发表期刊名称：2018 10th International Conference on Wireless Communications and Signal Processing, WCSP 2018

通讯作者：程星,朱正宇,史文哲,李洋

第一作者：刘得军

论文类型：会议论文

论文概要：程星,刘得军,朱正宇,史文哲,李洋，A ResNet-DNN based Channel Estimation and Equalization Scheme in FBMC/OQAM Systems，2018 10th International Conference on Wireless Communications and Signal Processing, WCSP 2018，2018，

论文题目：A ResNet-DNN based Channel Estimation and Equalization Scheme in FBMC/OQAM Systems