Distributed transmit-power control in cognitive radio networks using a hybrid-adaptive game-theoretic technique
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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.
CitationAFRICON, 2015 Date of Conference: 14-17 Sept. 2015 Page(s): 1 - 5
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