Computer Science ›› 2020, Vol. 47 ›› Issue (6A): 294-298.doi: 10.11896/JsJkx.190700097

• Computer Network • Previous Articles     Next Articles

Spectrum Occupancy Prediction Model Based on EMD Decomposition and LSTM Networks

ZHAO Xiao-dong1, SU Gong-Jin2, LI Ke-li2, CHENG Jie2 and XU Jiang-feng1   

  1. 1 School of Information Engineering,Zhengzhou University,Zhengzhou 450001,China
    2 Henan Radio Management Information System Backup Center,Zhengzhou 450000,China
  • Published:2020-07-07
  • About author:ZHAO Xiao-dong, born in 1991, master.His main research interest include big data, artificial intelligence, deep lear-ning.
    XU Jiang-feng, born in 1965, Ph.D.professor.His main research interest include big data, cryptography, database.
  • Supported by:
    This work was supported by the National Key Research and Development Program of China.

Abstract: Spectrum occupancy is an important basis to measure the spectrum utilization rate and reflect whether the spectrum allocation is reasonable.However,the unsteady spectrum occupancy sequence presents great challenges for effective prediction.In this paper,a new computing model (EMD-LSTM) combining EMD and LSTM is proposed.Firstly,the empirical mode decomposition (EMD) of the original occupancy sequence is used to generate the Intrinsic Mode Function (IMF) with different time scales,and then the highly correlated IMF is selected by Pearson correlation coefficient.Then,IMF and spectrum occupancy sequence are fused,and the occupancy sequence is predicted by using the long and short time memory network (LSTM).Simulation experiments and analysis show that,compared with the ordinary LSTM network,the new model has a great improvement in predicting the change of spectrum occupancy.

Key words: EMD, EMD-LSTM, Long-term and short-term memory network, Spectrum occupancy

CLC Number: 

  • TP183
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