高移动性Jakes信道的学习与估计

doi:10.12305/j.issn.1001-506X.2021.04.30

Abstract

Abstract:

In high mobility scenarios, the channel has the characteristics of fast time-varying and non-stationary, which poses a new challenge to the accurate channel estimation. For high mobility Jakes channel, a channel learning and estimation network based on image reconstruction and recovery principle is proposed. Firstly, according to the local correlation characteristics of Jakes channel matrix, a fast super-resolution convolution neural network is constructed to extract channel features, and channel image modeling is completed by channel interpolation. Then, the denoising neural network is used to reduce the influence of channel noise and further improve the estimation accuracy. Finally, the simulation results in time domain and frequency domain show that the proposed scheme performs better than the traditional algorithm. Compared with the latest methods based on deep learning, the proposed scheme also has performance advantages and faster convergence speed.

Key words: channel estimation, high mobility channel, deep learning, image denoising, super-resolution reconstruction

CLC Number:

TN911.72

Kai SHAO, Liancheng CHEN, Yin LIU. Learning and estimation of high mobility Jakes channel[J]. Systems Engineering and Electronics, 2021, 43(4): 1119-1125.

Figures/Tables 8

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Table 1

References 23

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算法	乘法运算次数	复杂度
LS	N	O(N)
LMMSE	3N³+N²	O(N³)
BEM-LS	4(QL)²N+QLN	O((QL)²N)
BEN-UKF	3N²QL+25(QL)²N	O(N²QL)
ChannelNet	107N²+23N	O(N²)
FSR-Net	324N²+28N	O(N²)

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Learning and estimation of high mobility Jakes channel

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