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A Game-Theoretic Approach for Cognitive Radio Networks using Machine Learning Techniques

EasyChair Preprint no. 2313

14 pagesDate: January 4, 2020

Abstract

Cognitive Radio has been viewed as a promising technology to enhance spectrum utilization significantly. In this work, we propose a model for Dynamic Spectrum Allocation in Cognitive Radio Networks using Game Theory. Furthermore, in order to accommodate for all cases, we have put to good use of Preemptive Resume Priority M|M|1 Queuing Model. To supplement it we introduce a priority-based scheduling algorithm called Incremental Weights-Decremental Ratios(IW-DR). As a means to ameliorate the efficiency, we have made use of Regression Models.

Keyphrases: cognitive networks, game theory, queueing theory, Regression, spectrum allocation

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
@Booklet{EasyChair:2313,
  author = {S. Mangairkarasi and Rooppesh Sarankapani and Deivasegamani Arivudainambi},
  title = {A Game-Theoretic Approach for Cognitive Radio Networks using Machine Learning Techniques},
  howpublished = {EasyChair Preprint no. 2313},

  year = {EasyChair, 2020}}
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