dc.contributor.author | Ondeng, Oscar | |
dc.contributor.author | Ouma, Heywood | |
dc.date.accessioned | 2016-06-07T12:24:24Z | |
dc.date.available | 2016-06-07T12:24:24Z | |
dc.date.issued | 2015 | |
dc.identifier.citation | AFRICON, 2015 Date of Conference: 14-17 Sept. 2015 Page(s): 1 - 5 | en_US |
dc.identifier.uri | http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7331975&tag=1 | |
dc.identifier.uri | http://hdl.handle.net/11295/96063 | |
dc.description.abstract | 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. | en_US |
dc.language.iso | en | en_US |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 United States | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/us/ | * |
dc.title | Distributed transmit-power control in cognitive radio networks using a hybrid-adaptive game-theoretic technique | en_US |
dc.type | Article | en_US |