Deep Stacked Autoencoder Based Long-Term Spectrum Prediction Using Real-World Data

Spectrum prediction is challenging due to its multi-dimension, complex inherent dependency, and heterogeneity among the spectrum data. In this paper, we first propose a stacked autoencoder (SAE) and bi-directional long short-term memory (Bi-LSTM) based spectrum prediction method (SAEL-SP). Specifica...

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Veröffentlicht in:	IEEE transactions on cognitive communications and networking 2023-06, Vol.9 (3), p.1-1
Hauptverfasser:	Pan, Guangliang, Wu, Qihui, Ding, Guoru, Wang, Wei, Li, Jie, Xu, Fuyuan, Zhou, Bo
Format:	Artikel
Sprache:	eng
Schlagworte:	Autoregressive processes Data models deep learning Feature extraction Frequency measurement Heterogeneity Hidden Markov models Predictive models Spatial data Spectrum prediction stacked autoencoder temporal-spectral-spatial correlations Time-frequency analysis
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