A Case Study of Primary User Arrival Prediction Using the Energy Detector and the Hidden Markov Model in Cognitive Radio Networks

Abstract

Cognitive Radio (CR) is considered a key enabler technology for applications that require high connectivity (e.g., Smart Cities and Internet of Things), mainly because of its spectrum sensing function. In this way, CRs can sense the spectrum environment to select the best available channel for communication and they can, potentially, use licensed spectrum bands as a Secondary User (SU). However, CR has to vacate the licensed spectrum band as soon as a Primary User (PU) intends to use the channel. In this way, one of the most challenging topics in CR is the PU arrival prediction. Therefore, this paper presents a real-data study case of PUs arrivals prediction using the Hidden Markov Model (HMM) in CR. Herein, the Energy Detector (ED) is used to detect the presence of PUs. Our results show that the traditional method of combining the ED with the HMM may not be suitable in CR networks.