Spectrum situation prediction for non-cooperative wireless networks

Abstract:

The spectrum situation prediction of non-cooperative wireless network in the complex electromagnetic environment is investigated. Based on machine learning theory, the three-dimensional characteristics of time, space, and frequency of collected spectrum situation data are extracted; the inherent correlations in the three-dimensional characteristics are fully data mined; and the spectrum prediction frameworks are built to predict frequency adjustment behavior of non-cooperative communication nodes. The results show that the single-step or multi-step prediction for the frequency can be performed on the frequency adjustment for future moments by exploiting the spectrum prediction frameworks as long as sufficient spectrum situation data can be intercepted when the frequency adjustment exists in the communication process of non-cooperative wireless networks. Therefore, the possible frequency used in the future for the target system can be accurately locked in. This work can provide key technical support for the subsequent communication tracking and interference tasks.