Abstract
In this paper, we introduce a new fuzzy reinforcement learning method to quality of service (QoS) provisioning cognitive transmission in cognitive radio networks. The cognitive transmissions under QoS constraints are treated here as the data sending at two different average power levels depending on the activity of the primary (licensed) users, which is determined by the secondary (unlicensed) users. For this transmission, the model is defined a state-transition model. The maximum throughput under these statistical QoS constraints is determined by using fuzzy QoS reinforcement learning techniques. The performance effectiveness of the proposed method is obtained in situations and comparison with the numerical method based on the effective capacity of the cognitive radio channel under various QoS constraints. It is shown that the hybrid AI method used outperforms comparable results obtained by the classical numerical method, including various situations with different QoS limitations.
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