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Whitespace prediction using hidden markov model based maximum likelihood classification

  • 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 Hidden Markov 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.

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Metadaten
Author:Ahmad Saad, Henning F. Schepker, Barbara Staehle, Rudi Knorr
URL:https://ieeexplore.ieee.org/document/8746327
DOI:https://doi.org/10.1109/VTCSpring.2019.8746327
ISBN:978-1-7281-1217-6
Parent Title (English):89th Vehicular Technology Conference (VTC2019-Spring), 28 April-1 May 2019, Kuala Lumpur, Malaysia
Publisher:IEEE
Document Type:Conference Proceeding
Language:English
Year of Publication:2019
Release Date:2020/01/16
Pagenumber:7
Note:
Volltextzugriff für Angehörige der Hochschule Konstanz möglich
Institutes:Fakultät Informatik
Relevance:Keine peer reviewed Publikation (Wissenschaftlicher Artikel und Aufsatz, Proceeding, Artikel in Tagungsband)
Open Access?:Nein
Licence (English):License LogoLizenzbedingungen IEEE