Evaluation of Radio Channel Utility using Epsilon-Greedy Action Selection

Evaluation of Radio Channel Utility using Epsilon-Greedy Action Selection

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This paper presents an algorithm that supports the dynamic spectrum access process in cognitive radio networks by generating a sorted list of best radio channels or by identifying those frequency BABY BODY LOTION ranges that are not in use temporarily.The concept is based on the reinforcement learning technique named Q-learning.To evaluate the utility of individual radio channels, spectrum monitoring is performed.In the presented solution, the epsilon-greedy action selection method is used to indicate which channel should be monitored next.The article includes a description of Chamomile Tea the proposed algorithm, scenarios, metrics, and simulation results showing the correct operation of the approach relied upon to evaluate the utility of radio channels and the epsilon-greedy action selection method.

Based on the performed tests, it is possible to determine algorithm parameters that should be used in this proposed deployment.The paper also presents a comparison of the results with two other action selection methods.