基扩展模型下基于LSTM神经网络的时变信道预测方法

doi:10.12305/j.issn.1001-506X.2022.09.33

Abstract

Abstract:

For high-speed mobile orthogonal frequency division multiplexing system, a time-varying channel prediction method based on long short-term memory (LSTM) neural network under basis expansion model (BEM) is proposed. To reduce the modeling error of the traditional BEM, according to the strong correlation characteristics of the wireless channel at the same location for the different trains in a high-speed mobile environment, the optimal basis function is obtained by the channel sate information of historical time, and it is used to model the channel. Then, the channel information at the future time is obtained by the offline training and online prediction of the channel base coefficient via LSTM neural network, which greatly reduces the computational complexity. In offline training, to enhance the practicality of the prediction model, the channel estimation, rather than the ideal channel information, is set to the approximation objective of the network. The simulation results show that the proposed method has lower computational complexity and better prediction accuracy comparing with the existing methods.

Key words: high-speed mobile, long short-term memory (LSTM) neural network, basis expansion model (BEM), time-varying channel prediction

CLC Number:

TN929.5

Qian NIE, Lihua YANG, Bo HU, Lulu REN. Time-varying channel prediction method based on LSTM neural networks under basis expansion model[J]. Systems Engineering and Electronics, 2022, 44(9): 2971-2977.

Figures/Tables 7

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References 30

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参数	数值
OFDMA符号长度(N)	128
循环前缀长度	16
载波频率/GHz	2.35
子载波间隔/kHz	15
列车速度/(km/h)	500
信道模型	5径莱斯信道
莱斯因子	5
基函数个数(Q)	4
隐藏层神经元数目(D₁、D₂)	10
小批量大小	64
神经元损失概率	0.2
最大迭代次数	250

训练样本数	训练时长/s
100	58.105 428
500	317.138 123
1 000	651.253 307
2 000	1 312.550 167

预测方法	复杂度(FLOPs)
文献[7]	获取历史信息	$O\left(22 N_p+N L \log _2(N L)\right) $	6 670
	线下训练	$ O\left(2 U\left(N D_1+D_1 D_2+D_2 N\right)\right)$	5.32×10⁶
	线上测试	$ O\left(2\left(N D_1+D_1 D_2+D_2 N\right)\right)$	5.32×10³
文献[9]	获取历史信息	$O\left(22 N_p+N L \log _2(N L)\right) $	6 670
	线下训练	$\begin{gathered}O\left(2 U L \left(4\left(N\left(D_1+D_2\right)+\left(D_1+D_2\right)^2+\left(D_1+D_2\right)\right)+\right.\right. \\\left.\left.4\left(\left(D_1+D_2\right)^3+\left(D_1+D_2\right)^4+\left(D_1+D_2\right)^2\right)+\left(\left(D_1+D_2\right)^2 N+N\right)\right)\right)\end{gathered} $	7.368 48×10⁹
	线上测试	$ \begin{gathered}O\left(2 L \left(4\left(N\left(D_1+D_2\right)+\left(D_1+D_2\right)^2+\left(D_1+D_2\right)\right)+\right.\right. \\\left.\left.4\left(\left(D_1+D_2\right)^3+\left(D_1+D_2\right)^4+\left(D_1+D_2\right)^2\right)+\left(\left(D_1+D_2\right)^2 N+N\right)\right)\right)\end{gathered}$	7.368 48×10⁶
新方法	获取历史信息	$O\left(N^2+4 L Q N_P+2 L Q\left(N_P-1\right)\right) $	20 184
	线下训练	$ \begin{gathered}O\left(2 U L \left(4\left(Q L\left(D_1+D_2\right)+\left(D_1+D_2\right)^2+\left(D_1+D_2\right)\right)+\right.\right. \\\left.\left.4\left(\left(D_1+D_2\right)^3+\left(D_1+D_2\right)^4+\left(D_1+D_2\right)^2\right)+\left(\left(D_1+D_2\right)^2 N+N\right)\right)\right)\end{gathered}$	1.456 416×10⁹
	线上测试	$\begin{gathered}O\left(2 L \left(4\left(Q L\left(D_1+D_2\right)+\left(D_1+D_2\right)^2+\left(D_1+D_2\right)\right)+\right.\right. \\\left.\left.4\left(\left(D_1+D_2\right)^3+\left(D_1+D_2\right)^4+\left(D_1+D_2\right)^2\right)+\left(\left(D_1+D_2\right)^2 N+N\right)\right)\right)\end{gathered} $	1.456 416×10⁶

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Time-varying channel prediction method based on LSTM neural networks under basis expansion model

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