基于深度学习的正交频分复用系统信道估计

doi:10.11959/j.issn.1000-436x.2023240

Abstract

Abstract:

An efficient channel estimation model based on deep learning was proposed for the problems of inter-carrier interference and inter-symbol interference in 5G system signal reception.The estimated channels were obtained through a preliminary estimation at the pilots.And they were treated as low resolution images containing noise, which were input into the channel estimation model.By learning the mapping relationship between the low resolution images and the high resolution images, the noise in input channels was removed, and the high-resolution channel images were restored to obtain the entire channel state information eventually.The simulation results show that the model not only continues the advantages of traditional attention mechanisms in suppressing redundant information, reduces computational overhead, but also achieves good accuracy and robustness, and has good estimation performance for various channels.

Key words: deep learning, channel estimation, image restoration, attention mechanism

CLC Number:

TN92

Yun ZHANG, Jing ZHOU, Jingwei HUANG, Shujuan YU, Liya HUANG. Channel estimation for OFDM system based on deep learning[J]. Journal on Communications, 2023, 44(12): 124-133.

Figures/Tables 18

References 26

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参数	参数值
系统带宽/MHz	1.6
载波频率/GHz	2.1
用户设备速度/(km·h^-1)	50
子载波数	72
子符号数	14
导频数	48

模块	层数	滤波器数量	滤波器大小	激活函数
SRCNN	1	64	9×9	ReLU
	2	32	1×1	ReLU
	3	2	5×5	—
DNCNN	4	64	3×3	ReLU
	5～22	64	3×3	ReLU
	23	2	3×3	—

参数名称	参数取值
载波频率/GHz	28
采样频率/MHz	100
接收天线数	16
发射天线数	32
子载波数	64
视距角	60°
基站天线高度/m	10
主路径数	1～4
最大多普勒频移/Hz	0,500,1 000

层数	滤波器数量	滤波器大小	激活函数
1～9	64	3×3	ReLU
10	2	3×3	Tanh

算法	参数量/个	浮点运算/FLOP
ChannelNet	0.8×10⁶	3 7620 9408
DNCNN	0.7×10⁶	3 4386 7392
Deep CNN	0.04×10⁶	1 1442 5856
AAB	0.05×10⁶	2541 6100
PDAAB	1.1×10⁶	3352 3364

Channel estimation for OFDM system based on deep learning

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