Deep Learning-Based V2V Channel Estimations Using VNETs

被引：0

作者：

Song, Qi ^{[1
]}

Lan, Tian ^{[1
]}

Tian, Xuanxuan ^{[1
]}

Zhang, Tingting ^{[1
]}

机构：

[1] Harbin Inst Technol, Shenzhen Grad Sch, Commun Engn Res Ctr, Shenzhen 518055, Peoples R China

来源：

COMMUNICATIONS, SIGNAL PROCESSING, AND SYSTEMS, CSPS 2018, VOL III: SYSTEMS | 2020年 / 517卷

关键词：

Channel estimation; Neural network; OFDM; Deep learning; CNN;

D O I：

10.1007/978-981-13-6508-9_24

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The development of cooperative intelligent transportation systems brings new challenges to wireless communication technologies, where the channel estimation becomes more and more important. In this paper, a novel data-driven channel estimation method based on deep learning framework is adopted. Based on the feedforward neural network, the VNET neural network based on the convolutional neural network is proposed. The simulations and practical measurements are also provided to verify the performance advantages. The results show the achieved performance advantages of the proposed VNET-based method, which is shown to be an effective solution.

引用

页码：184 / 192

页数：9

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