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Predicting target data rates for dynamic spectrum allocation using Gaussian process regression
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DOI：10.1016/j.icte.2021.08.011.png)
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Abstract
En 中文
Issues in spectrum allocation between wireless network users have arisen due to the fast increase in the number of broadband services. Such issues include the failure to maximize the performance of all users by considering only a particular category of users. Specifically, a previously adopted selfish algorithm for spectrum allocation considers only the performance of the weakest user. To resolve this issue, we propose a new target data rate setting algorithm for dynamic spectrum allocation. In this algorithm, a Gaussian process regression model is trained to predict the target data rate. All users that perform below the defined target rate will have their frequency band allocations changed to one that guarantees a better performance. Through simulations, we show that the maximum data rate achieved by the weakest user in our algorithm is 121.7% higher than the selfish algorithm. (C) 2021 The Korean Institute of Communications and Information Sciences (KICS). Publishing services by Elsevier B.V.
Keywords:
Target data rate
Gaussian process regression
Spectrum allocation
Machine learning
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