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.
| Author: | Ahmad Saad, Henning F. Schepker, Barbara Staehle, Rudi Knorr |
|---|---|
| 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 |
| Page Number: | 7 |
| Note: | Volltextzugriff für Angehörige der Hochschule Konstanz möglich |
| Institutes: | Fakultät Informatik |
| Open Access?: | Nein |
| Licence (German): |  Urheberrechtlich geschützt |
