NDLI: Dynamic spectrum allocation in cognitive radio using hidden Markov models: Poisson distributed case

Content Provider	IEEE Xplore Digital Library
Author	Akbar, I.A. Tranter, W.H.
Copyright Year	2007
Description	Author affiliation: Mobile & Portable Radio Res. Group, Virginia Tech, Blacksburg, VA (Akbar, I.A.)
Abstract	Cognitive radio networks can be designed to manage the radio spectrum more efficiently by utilizing the spectrum holes in primary users' licensed frequency bands. Recent studies have shown that the radio spectrum is poorly utilized by the licensed users even in urban geographical areas. This spectrum utilization can be improved significantly by making it possible for secondary users (who are not being served by the primary system) to access spectrum holes, i.e., frequency bands not used by licensed users. In this novel work, we use hidden Markov models (HMMs) to model and predict the spectrum occupancy of licensed radio bands. The proposed technique can dynamically select different licensed bands for its own use with significantly less interference from and to the licensed users. It is found that by predicting the duration of spectrum holes of primary users, the CR can utilize them more efficiently by leaving the band, that it currently occupies, before the start of traffic from the primary user of that band. We propose a simple algorithm, called the Markov-based channel prediction algorithm (MCPA), for dynamic spectrum allocation in cognitive radio networks. In this work, we present the performance of our proposed dynamic spectrum allocation algorithm when the channel state occupancy of primary users are assumed to be Poisson distributed. The impact of CR transmission on the licensed users is also presented. It is shown that significant SIR improvements can be achieved using HMM based dynamic spectrum allocation as compared to the traditional CSMA based approach. The results obtained using HMM are very promising and HMM can offer a new paradigm for predicting channel behavior in cognitive radio, an area that has been of much research interest lately.
Starting Page	196
Ending Page	201
File Size	272862
Page Count	6
File Format	PDF
ISBN	1424410282
DOI	10.1109/SECON.2007.342884
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-03-22
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Cognitive radio Hidden Markov models Frequency Chromium Heuristic algorithms Radio spectrum management Predictive models Interference Telecommunication traffic Prediction algorithms
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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