Channel estimation and detection method for multicarrier system based on deep learning

被引：0

作者：

Wang Z.-F. ^{[1
]}

Yuan W.-N. ^{[1
]}

机构：

[1] School of Information Science and Engineering, East China University of Science and Technology, Shanghai

来源：

Zhejiang Daxue Xuebao (Gongxue Ban)/Journal of Zhejiang University (Engineering Science) | 2020年 / 54卷 / 04期

关键词：

Channel estimation; Deep learning; Filter bank multicarrier (FBMC); Neural network; Symbol detection;

D O I：

10.3785/j.issn.1008-973X.2020.04.012

中图分类号：

学科分类号：

摘要：

The system framework and imaginary interference were analyzed aiming at the problem of symbol detection and channel estimation in order to effectively improve the communication quality of filter bank multicarrier (FBMC) system. A channel estimation and detection method for FBMC system was proposed based on deep learning. A complete simulation system was established by combining FBMC-offset quadrature amplitude modulation (OQAM) with deep learning model, and the characteristics and label processing of received data were designed. ResNet-DNN neural network was used to model the channel symbol detection module. The original model structure and optimized model parameters were improved, which improved the accuracy of symbol detection compared with traditional classifiers. CNN+NN model was used to model and integrate for estimating, equalizing and detecting channel symbols. The theoretical analysis and simulation results show that the new method is superior to orthogonal frequency division multiplexing (OFDM) system and FBMC system based on pilot estimation in terms of noise resistance, robustness and bit error rate (BER) performance. © 2020, Zhejiang University Press. All right reserved.

引用

页码：732 / 738

页数：6

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