Optimal Sensing for Opportunistic Spectrum Access in Cognitive Radio

One of the most difficult things but important problem when designing an OSA (Opportunistic Spectrum Access) MAC protocol is how the unlicensed users decide when and which channel they should sense and access without conflicting the communications among PUs. To solve this problem, the unlicensed use...

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Bibliographic Details
Main Authors: Armi, Nasrullah, M Saad, M Naufal, Yusoff, Mohd Zuki, Arshad, Muhammad
Format: Article
Published: CSC Publisher 2010
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Online Access:http://www.cscjournals.org/csc/engineering_sciences.php
http://eprints.utp.edu.my/3599/
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Institution: Universiti Teknologi Petronas
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Summary:One of the most difficult things but important problem when designing an OSA (Opportunistic Spectrum Access) MAC protocol is how the unlicensed users decide when and which channel they should sense and access without conflicting the communications among PUs. To solve this problem, the unlicensed users should have the ability of adaptively and dynamically seeking and exploiting opportunities in both licensed and unlicensed spectrum and along both of the time and the frequency dimensions. Secondary Users (SUs) as unlicensed users are required to sense radio frequency band, and when PU are detected, they must vacate the channel immediately within certain amount of time. Due to hardware and energy constraints, full spectrum availability cannot be sensed as well as they do not monitor when there is no data to be transmitted. In this paper, we study MAC protocol design and optimal sensing for OSA in Cognitive Radio (CR) ad hoc network under Partially Observable Markov Decision Process (POMDP) algorithm that maximizes achievable throughput for SUs with sufficient protection to PUs. The bandwith effect to number of bit transmitted in one slot and tractable greedy algorithm to reduce the complexity of POMDP calculation was studied as well. The derivation of greedy approach proves that sensing problem can be solved either optimally or approximate the optimal solution. Computer simulation is used to evaluate the performances both of optimal and sub optimal strategy.