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A deep learning-aided temporal spectral ChannelNet for IEEE 802.11p-based channel estimation in vehicular communications
EURASIP Journal on Wireless Communications and Networking ( IF 2.3 ) Pub Date : 2020-05-11 , DOI: 10.1186/s13638-020-01714-4
Xuchen Zhu , Zhichao Sheng , Yong Fang , Denghong Guo

In vehicular communications using IEEE 802.11p, estimating channel frequency response (CFR) is a remarkably challenging task. The challenge for channel estimation (CE) lies in tracking variations of CFR due to the extremely fast time-varying characteristic of channel and low density pilot. To tackle such problem, inspired by image super-resolution (ISR) techniques, a deep learning-based temporal spectral channel network (TS-ChannelNet) is proposed. Following the process of ISR, an average decision-directed estimation with time truncation (ADD-TT) is first presented to extend pilot values into tentative CFR, thus tracking coarsely variations. Then, to make tentative CFR values accurate, a super resolution convolutional long short-term memory (SR-ConvLSTM) is utilized to track channel extreme variations by extracting sufficiently temporal spectral correlation of data symbols. Three representative vehicular environments are investigated to demonstrate the performance of our proposed TS-ChannelNet in terms of normalized mean square error (NMSE) and bit error rate (BER). The proposed method has an evident performance gain over existing methods, reaching about 84.5% improvements at some high signal-noise-ratio (SNR) regions.



中文翻译:

一种深度学习辅助的时间频谱ChannelNet,用于车辆通信中基于IEEE 802.11p的信道估计

在使用IEEE 802.11p的车辆通信中,估计信道频率响应(CFR)是一项极具挑战性的任务。信道估计(CE)的挑战在于,由于信道和低密度导频的极快时变特性,跟踪CFR的变化。为了解决这个问题,受图像超分辨率(ISR)技术的启发,提出了一种基于深度学习的时间频谱信道网络(TS-ChannelNet)。在ISR的过程之后,首先提出带有时间截断的平均决策导向估计(ADD-TT),以将导频值扩展到暂定CFR中,从而粗略地跟踪变化。然后,为了使准确的CFR值准确,超分辨率卷积长短期存储器(SR-ConvLSTM)用于通过提取数据符号的足够时域频谱相关性来跟踪信道极端变化。对三个代表性的汽车环境进行了研究,以证明我们提出的TS-ChannelNet在归一化均方误差(NMSE)和误码率(BER)方面的性能。与现有方法相比,该方法具有明显的性能提升,在某些高信噪比(SNR)区域达到了约84.5%的改进。

更新日期:2020-05-11
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