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The primary objective of cooperation in Cognitive Radio (CR) networks is to increase the efficiency and improve the network performance. However, CR users may act destructively and decrease both their own and others' performances. This can be due to Byzantine adversaries or unintentional erroneous conduct in cooperation. This paper presents an autonomous cooperation solution for each CR user, i.e., each CR user decides with whom to cooperate. The proposed solution is to increase the spectrum access in cooperative CR networks. To realize this, a Reinforcement Learning (RL) algorithm is utilized to determine the suitability of the available cooperators and select the appropriate set of cooperators.
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