Title:

Evaluation of Radio Channel Utility using Epsilon-Greedy Action Selection, Journal of Telecommunications and Information Technology, 2021, nr 3

Creator:

Malon, Krzysztof

Subject and Keywords:

cognitive radio ; machine learning ; dynamic spectrum access ; spectrum monitoring ; Q-learning

Description:

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 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 proposed deployment. The paper also presents a comparison of the results with two other action selection methods

Publisher:

Instytut Łączności - Państwowy Instytut Badawczy

Date:

2021, nr 3

Resource Type:

artykuł

Format:

application/pdf

Resource Identifier:

ISSN 1509-4553, on-line: ISSN 1899-8852

Source:

Journal of Telecommunications and Information Technology

Language:

ang

Rights Management:

Biblioteka Naukowa Instytutu Łączności