Wireless spectrum occupancy prediction method based on LSTM network

A wireless spectrum and prediction method technology, applied in the field of wireless spectrum occupancy prediction based on LSTM network, can solve the problem of single applicability of wireless spectrum occupancy prediction method

Active Publication Date: 2019-11-26
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

[0006] In view of the above-mentioned problems or deficiencies, in order to solve the relatively single and applicability problems of existing wireless spectrum occupancy prediction methods, the present invention provides a wireless spectrum occupancy prediction method based on LSTM network, by fully combining traditional spectrum occupancy prediction The advantages of the method and neural network can effectively realize the prediction of spectrum occupancy, and can take into account the extraction of linear information and nonlinear information and the processing of non-stationary sequences

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  • Wireless spectrum occupancy prediction method based on LSTM network
  • Wireless spectrum occupancy prediction method based on LSTM network

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[0030] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments.

[0031] Step 1) Modeling steps such as figure 1 , ACF in the figure represents the autocorrelation coefficient, and PACF represents the partial autocorrelation coefficient. If the modeling process has carried out the difference operation, the final analysis result is obtained through difference reduction; otherwise, the prediction result is directly obtained.

[0032] Step 3) From figure 1It can be seen from that, if the spectrum occupancy observation sequence is a non-stationary sequence, it is transformed into a stationary sequence by one or more order difference operations; if it is a stationary sequence, proceed to the next step directly. When the spectrum occupancy sequence is converted into a stationary sequence, the ARIMA model becomes an ARMA model, so the Harvey transformation method is used to transform the ARMA model into a state-s...

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Abstract

The invention relates to the field of wireless spectrum analysis, in particular to a wireless spectrum occupancy prediction method based on an LSTM network. According to the method, the ARIMA model and the Kalman filtering algorithm are combined to overcome the limitation of the ARIMA model, and the initial value of the Kalman filtering algorithm is determined by the ARIMA model and complements each other. Considering that the LSTM neural network has a very strong capturing capability on the nonlinear relationship,, an ARIMA, Kalman and LSTM combined prediction model is constructed, namely, alinear relationship existing in frequency band occupancy rate sequence data is extracted by utilizing an ARIMA and Kalman hybrid model, an unextracted nonlinear part in residual errors of the hybrid model is extracted by LSTM, and fitting information is superposed into the ARIMA and Kalman hybrid model. According to the method, the advantages of ARIMA, Kalman and LSTM networks are combined, the stationary sequence can be analyzed, the non-stationary sequence can also be analyzed, and meanwhile linear and nonlinear information can be well extracted.

Description

technical field [0001] The invention relates to the field of wireless spectrum analysis, in particular to an LSTM network-based wireless spectrum occupancy prediction method, which uses LSTM to predict wireless spectrum occupancy, and uses ARIMA combined with Kalman's hybrid model to improve it. Background technique [0002] At present, there are many methods for application and spectrum prediction, which can be summarized into two categories: [0003] 1) Based on statistical analysis methods: In 2012, Wang Lei et al. aimed at the problem that the traditional spectrum occupancy autoregressive moving average (ARMA) model could not accurately describe the nonlinear time-varying characteristics of the spectrum occupancy state because it did not consider the conditional second-order moment of the sequence. This paper proposes a time series modeling method of spectrum occupancy state based on Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH) process. ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50G06N3/04G06N3/08
CPCG06N3/08G06N3/044G06N3/045
Inventor 吕幼新胡幸蔡青飞王鑫唐甜练祥张巍张杰
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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