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A Combined Throughput and Hierarchical Strategies for Medium Access in Wireless Network

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Recently, the selfish behavior of mobile users in MAC protocols has been widely analyzed using game theory with all its powerful solution concepts. This selfish behavior causes the lost and collision of more packets. To reduce this phenomenon and to enhance the performance of the networks, in this paper, we propose to combine the learning algorithm, with a partial cooperation in slotted Aloha mechanism. We develop a Stackelberg game where the base station is the leader, and all mobiles are followers. The leader chooses first its strategy and broadcasts it to the followers. We give to the leader the choice of being either selfish by maximizing its own throughput, or being altruistic by maximizing the followers throughputs.  We model the system by a three dimensional Markov chain. The states of the Markov chain describe the number of backlogged packets among leader and followers. After, we introduce a learning process, and we study its impact on the system performance. The obtained results showed that this approach has significantly improved the partial cooperative slotted Aloha mechanism and give best results for the utility.
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Medium Access; Hierarchical Strategies; Stackelberg Game; Learning Algorithms

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