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

EasyChair Preprint 2313

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

@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 2313},
  year      = {EasyChair, 2020}}