DocumentCode :
3099192
Title :
Aligning Spectrum-User Objectives for Maximum Inelastic-Traffic Reward
Author :
Hamdaoui, Bechir ; NoroozOliaee, MohammadJavad ; Tumer, Kagan ; Rayes, Ammar
Author_Institution :
Oregon State Univ., Corvallis, OR, USA
fYear :
2011
fDate :
July 31 2011-Aug. 4 2011
Firstpage :
1
Lastpage :
6
Abstract :
We develop objective functions for large-scale distributed dynamic spectrum access (DSA) networks that, by means of any learning algorithm, enable DSA users to locate and exploit spectrum opportunities effectively, thereby increasing their achieved throughput (or "rewards" to be more general). We show that the proposed functions are: (i) optimal by enabling users to achieve high rewards, (ii) scalable by performing well in systems with a small as well as a large number of users, (iii) learnable by allowing users to reach up high rewards very quickly, and (iv) distributed by being implementable in a decentralized manner.
Keywords :
cognitive radio; learning (artificial intelligence); telecommunication computing; telecommunication network reliability; telecommunication traffic; large-scale distributed DSA network; large-scale distributed dynamic spectrum access network; learning algorithm; maximum inelastic-traffic reward; spectrum-user objective; Algorithm design and analysis; Joints; Predictive models; Quality of service; Sensitivity; Switches;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Communications and Networks (ICCCN), 2011 Proceedings of 20th International Conference on
Conference_Location :
Maui, HI
ISSN :
1095-2055
Print_ISBN :
978-1-4577-0637-0
Type :
conf
DOI :
10.1109/ICCCN.2011.6005926
Filename :
6005926
Link To Document :
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