A wireless spectrum occupancy prediction method based on lstm network

Abstract

The invention relates to the field of wireless spectrum analysis, in particular to an LSTM network-based wireless spectrum occupancy prediction method. The invention combines the ARIMA model and the Kalman filter algorithm to overcome the limitations of the ARIMA model, and the initial value of the Kalman filter algorithm is determined by the ARIMA model, which complements each other. Considering that the LSTM neural network has a strong ability to capture nonlinear relationships, the present invention will construct a combined prediction model of ARIMA, Kalman and LSTM, that is, use the ARIMA and Kalman hybrid model to extract the linear relationship existing in the frequency band occupancy sequence data, and the LSTM will The unextracted nonlinear part of the remaining residual of the mixed model is extracted, and the fitting information is superimposed into the ARIMA and Kalman mixed model. Therefore, the present invention combines the advantages of ARIMA, Kalman and LSTM networks, can analyze both stationary sequences and non-stationary sequences, and can also extract linear and nonlinear information well.

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. ...

AI Technical Summary

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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