Distributed transmit-power control in cognitive radio networks using a hybrid-adaptive game-theoretic technique
Date
2015Author
Ondeng, Oscar
Ouma, Heywood
Type
ArticleLanguage
enMetadata
Show full item recordAbstract
This paper studies game-theoretic distributed transmit-power control in a cognitive radio network. It presents a hybrid-adaptive algorithm that interfaces Iterative Water-Filling with two learning algorithms: the Hedging Algorithm and the Historical Matching Algorithm. Iterative Water-Filling helps achieve a fast convergence whereas the learning algorithms help guard against exploitation. The learning algorithms employed are selected based on their performance in deterministic and probabilistic network environments. The hybrid-adaptive algorithm is shown to offer improvements on other methods published. It also performs better than Iterative Water-Filling and the learning algorithms taken in isolation. The main metric is the utility achieved by the players in the game-theoretic setting.
URI
http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7331975&tag=1http://hdl.handle.net/11295/96063
Citation
AFRICON, 2015 Date of Conference: 14-17 Sept. 2015 Page(s): 1 - 5Rights
Attribution-NonCommercial-NoDerivs 3.0 United StatesUsage Rights
http://creativecommons.org/licenses/by-nc-nd/3.0/us/Collections
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