基于深度学习的广义频分复用系统时频双选择信道估计

doi:10.11959/j.issn.1000-436x.2021188

Abstract

Abstract:

There exist intrinsic inter-carrier interference and inter-subsymbol interference in generalized frequency division multiplexing (GFDM) systems.Under condition of time-frequency doubly selective channels, severe effects of pilot contamination would occur and lead to significant performance degradation for the pilot-based channel estimations.To this end, a channel estimation framework for GFDM systems based on deep learning was proposed, which took the low-resolution image constructed with the least squares estimated channel gains of the pilot symbols as input.Consequently, a high-resolution image about the channel time-frequency response was recovered through a deep residual network, and the channel estimation was achieved for GFDM systems.A simulation system for the proposed GFDM time-frequency doubly selective channel estimation algorithm based on deep residual network was developed, and the optimal parameters of the deep residual network were determined through an offline training process.Simulation results show that the proposed algorithm can achieve better performance near to minimum mean square error (MMSE) estimation in terms of estimation error and bit error rate (BER), and has robust Doppler frequency shift generalization capability.

Key words: generalized frequency division multiplexing (GFDM), deep learning, doubly-selective channel, channel esti-mation, residual network

CLC Number:

TN92

Ying WANG, Jun REN, Ke SHI, Bin LIN. Doubly-selective channel estimation for generalized frequency division multiplexing systems based on deep learning[J]. Journal on Communications, 2021, 42(10): 233-242.

Figures/Tables 14

方法	参数数量	实数乘法次数
CE-DNN	$\sum_{l = 1}^{L - 1} (N_{l} + 1) N_{l + 1}$	$\sum_{l = 1}^{L - 1} N_{l} N_{l + 1}$
MMSE	$K M \| P \| + \| P \|^{2}$	$4 K M (\| P \|^{2} + \| P \|)$
CE-ResNet	$\sum_{l = 1}^{L - 1} (k_{l, h} k_{l, w} k_{l, c} + 1) C_{l + 1}$	$\sum_{l = 1}^{L - 1} k_{l, h} k_{l, w} k_{l, c} H_{l + 1} W_{l + 1} C_{l + 1}$

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参数名称	参数取值
系统带宽/MHz	10
子载波数	K=64
子符号数	M=13
导频符号数目	K_p×M_p=22×4
原型滤波器	根升余弦函数
滚降因子α	0.1
调制方式	4-QAM
时变信道模型	Jakes
多径时延/ns	[0, 300, 500, 600, 800]
路径功率/dB	[0, -8, -17, -21, -25]

Doubly-selective channel estimation for generalized frequency division multiplexing systems based on deep learning

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方法	参数数量	实数乘法次数
MMSE	80 960	26 064 896
CE-DNN	8 774 164	8 768 000
CE-ResNet	53 298	4 883 200