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 viqua-f4 list of best radio channels or by identifying those frequency 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 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 orange zinger tomato proposed deployment.The paper also presents a comparison of the results with two other action selection methods.