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  4. Whitespace Prediction Using Hidden Markov Model Based Maximum Likelihood Classification
 
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2019
Konferenzbeitrag
Titel

Whitespace Prediction Using Hidden Markov Model Based Maximum Likelihood Classification

Abstract
The cornerstone of cognitive systems is environment awareness which enables agile and adaptive use of channel resources. Whitespace prediction based on learning the statistics of the wireless traffic has proven to be a powerful tool to achieve such awareness. In this paper, we propose a novel HiddenMarkov Model (HMM) based spectrum learning and prediction approach which accurately estimates the exact length of the whitespace in WiFi channels within the shared industrial scientific medical (ISM) bands. We show that extending the number of hidden states and formulating the prediction problem as a maximum likelihood (ML) classification leads to a substantial increase in the prediction horizon compared to classical approaches that predict the immediate (short-term) future. We verify the proposed algorithm through simulations which utilize a model for WiFi traffic based on extensive measurement campaigns.
Author(s)
Saad, Ahmad
Fraunhofer-Institut für Eingebettete Systeme und Kommunikationstechnik ESK
Schepker, Henning F.
Fraunhofer-Institut für Eingebettete Systeme und Kommunikationstechnik ESK
Staehle, Barbara
Hochschule Konstanz Technik, Wirtschaft und Gestaltung -HTWG-, Konstanz
Knorr, Rudi
Fraunhofer-Institut für Eingebettete Systeme und Kommunikationstechnik ESK
Hauptwerk
IEEE 89th Vehicular Technology Conference, VTC-Spring 2019. Proceedings
Konferenz
Vehicular Technology Conference (VTC Spring) 2019
Thumbnail Image
DOI
10.1109/VTCSpring.2019.8746327
Language
Englisch
google-scholar
ESK
Tags
  • cognitive Radio

  • whitespace prediction...

  • Hidden Markov Model

  • HMM

  • maximum likelihood

  • ML

  • maximum likelihood cl...

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