인컴번트 사용자 채널 점유확률 변화에 따른 SVM 기반의 무선 트래픽 이용환경 추론기법

Abstract

In this paper, we propose a case-based reasoning Cognitive Radio(CR) engine that uses limited resources efficiently in military tactical wireless communication environments. A CR engine should be able to learn and infer, and thus predict the available channel information of a secondary user based on information about traffic usage. To be able to do so, a low probability of channel collision should be associated with the engine. The engine should, thereby, be able to indicate the probability of channel occupancy of an incumbent user who requires interference protection. We used a Support Vector Machine(SVM) to measure the histogram-type wireless traffic usage environment in accordance with the change in the probably of channel occupancy of the incumbent user. SVM is a sorting algorithm of machine learning. Next, we calculated the histogram