2020 5th International Conference on Computer and Communication Systems (ICCCS) · 2020 · 18 citations · 10 references
Spectrum PredictionEngineeringMachine LearningSpectrum EstimationSpectrum SensingSmart Wireless NetworkFrequency BandData ScienceData MiningPattern RecognitionFrequency ManagementPrediction ModellingPredictive AnalyticsKnowledge DiscoveryIntelligent ClassificationSignal ProcessingData ClassificationSpectrum Prediction MethodSpectral AnalysisSpeech ProcessingSpectrum Prediction Technology
Spectrum prediction technology is able to make predictions about future use of the spectrum based on historical spectrum sensing data. ISM frequency bands for industrial, scientific and medical are part of the spectrum resources available to anyone without authorization, which makes this frequency band's spectrum dynamic with highly burstiness, and difficult to discover the spectrum pattern and predict. In this paper, data analysis and feature mining are carried out on the real collected Wi-Fi data. According to the features of Wi-Fi data, a classification prediction framework based on LSTM model is developed. This algorithm framework can establish several classification predictors according to the features of Wi-Fi data mining and effective classification algorithms. In the process of prediction, the corresponding predictor is selected dynamically according to the characteristics of the history data. Experimental results with real data show that this prediction framework can effectively improve the accuracy of spectrum prediction.
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