Paper Info
Title
Hidden Markov Model Spectrum Predictor for Poisson Distributed Traffic
Abstract
Static spectrum allocation policies allied with the increasing demand for higher data rates stimulated the pursuit of alternative spectrum allocation strategies. In this context, opportunistic Spectrum Access (OSA) has been considered an alternative to allow licensed portions of the spectrum to be shared with unlicensed users. OSA requires unlicensed users to identify unused portions of the spectrum for opportunistic access that minimizes possible interference with the licensed users. Accurate mechanisms to avoid interference and improve the spectrum usage is highly desirable. This work investigates the performance of a traditional Hidden Markov Model (HMM) predictor where the licensed traffic follows a Poisson distribution. The results show that, under the evaluated settings, traditional HMM predictor can improve spectrum usage up to 15% at the expense of a high rate ($\approx$ 50%) of inaccurate forecasts of idle periods. Based on these results, this paper proposes two techniques to optimize prediction performance and reduce inaccurate forecasts rate. Using the proposed enhancements inaccurate forecasts rate was reduced to only 6%. An additional benefit was observed when reducing collision with the licensed user, that is an improvement in the amount of slots effectively used by the unlicensed users from 15% to 23%.
Year
DOI
Venue
2020
10.1109/WiMob50308.2020.9253397
2020 16th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)
Keywords
DocType
ISSN
Hidden Markov Model,opportunity forecasting,Poisson Distributed Traffic,Cognitive Radio,opportunistic spectrum access
Conference
2160-4886
ISBN
Citations 
PageRank 
978-1-7281-9723-4
0
0.34
References 
Authors
9
4
Name
Order
Citations
PageRank
Rodrigo F. Bezerra100.34
Jacir Luiz Bordim25718.86
Marcus Vinicius Lamar322.07
Marcos F. Caetano4156.49