基于元学习的时变信道估计方法

doi:10.12305/j.issn.1001-506X.2023.06.32

Abstract

Abstract:

In the existing deep-learning-based channel estimation methods, there are problems such as large training data and huge time overhead, and the offline training network cannot adapt to the actual real-time changing channel environment. To overcome the problems above, a meta-learning-based time-varying channel estimation method in high-speed mobile orthogonal frequency division multiplexing (OFDM) system is proposed in this paper. The model-agnostic meta-learning (MAML) method is used for offline training for different sub-tasks, so that the network can adequately learn the characteristics of channel transmission and have the ability to quickly adapt to new tasks only by few training samples, which makes it have low computational complexity. In offline training, the proposed method sets the training target of the network as channel estimation with high accuracy rather than ideal channel information, which enhances the practicability of the estimation model. In addition, only the received pilot signal is used for offline training and online estimation, which reduces the number of network input samples and further reduces the computational complexity. Simulation results show that the proposed method has high estimation accuracy and low computational complexity, and it can quickly adapt to the new channel environment, which is suitable for time-varying channels acquisition in high-speed mobile communication systems.

Key words: high-speed mobile, orthogonal frequency division multiplexing(OFDM), time-varying channel estimation, meta-learning

CLC Number:

TN929.5

Lihua YANG, Lulu REN, Bo HU, Yongqi SHAO, Qian NIE. Meta-learning based time-varying channel estimation method[J]. Systems Engineering and Electronics, 2023, 45(6): 1872-1879.

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References 31

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参数	取值
调制方式	QPSK与16QAM
子载波数	128
CP长度	16
导频数	32
均衡方法	迫零(ZF)
载波频率/GHz	2.35
载波间隔/kHz	15
归一化多普勒频移f_d	0.003∶0.003∶0.3
信道任务数U	100
基础学习器学习率α	0.001
元学习器学习率β	0.003
微调网络学习率γ	0.001

抽头数	时延/ns	功率/dB	多普勒谱
1	0	0	莱斯
2	710	-6.51	经典
3	1 090	-16.27	经典
4	1 730	-25.71	经典
5	2 910	-29.31	经典

估计方法		复杂度/FLOPs
估计方法		每秒浮点运算次数	计算复杂度
基于BP	线下训练	8G(NE)	2.621 44×10⁸
基于BP	线上估计	4NE	1.310 72×10⁵
LSTM	线下训练	2G(4(2NE+E²+2N)+4N²+2N)	1.182 208×10⁹
LSTM	线上估计	4(2NE+E²+2N)+4N²+2N	5.911 04×10⁵
新方法	线下训练	4G(N_pE)	3.276 8×10⁷
新方法	线上估计	4N_pE	3.276 8×10⁴

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Meta-learning based time-varying channel estimation method

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