新型的基于堆栈式ELM的时变信道预测方法

doi:10.12305/j.issn.1001-506X.2022.02.37

Abstract

Abstract:

Aiming at the orthogonal frequency division multiplexing (OFDM) system under high-speed mobile scenario, a novel stacked extreme learning machine (ELM) based time-varying channel prediction method is proposed. Based on the single hidden layer neural network, to capture the deep information of the input data, the ELM method is firstly used to extract the deep features from the historical channel and obtain the initial output weight of the network. Then, to adapt to the channel changes, the proposed method updates the output weights of the network in real time based on the newly constructed historical channel samples and the initial output weights, and obtains the channel at the current moment based on the updated output weights.Finally, the simulation results shav that compared with the existing schemes, the proposed method has high prediction accuracy and is suitable for high-speed mobile scenarios.

Key words: high-speed mobility, orthogonal frequency division multiplexing (OFDM), time-varying channel prediction, stacked extreme learning machine (ELM), output weight update

CLC Number:

TN929.5

Jie ZHANG, Lihua YANG, Qian NIE. Novel time-varying channel prediction method based on stacked ELM[J]. Systems Engineering and Electronics, 2022, 44(2): 662-667.

Figures/Tables 6

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Table 1

References 30

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方法	线下训练	线上预测
文献[18]	U[Q(P+D)+Q]	Q(P+D)+Q
文献[19]	JU[Q(2P+D)+Q+D+P²]	J[Q(2P+D)+Q+D+P²]
文献[20]	U[Q(P+D)+Q]	2Q⁴+Q³+4Q²+DQ+2Q+P
本文方法	JU[Q(2P+D)+Q+D+P²]	2Q⁴+Q³+4Q²+DQ+2Q+P

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Novel time-varying channel prediction method based on stacked ELM

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