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

A Joint Design of User Modeling and Resource Management in Cognitive Radio Networks The limited available spectrum and inefficient spectrum utilization make it necessary to employ dynamic spectrum access techniques. The key enabling technology for dynamic spectrum access is cognitive radio (CR), which exploits the existing wireless spectrum opportunistically and utilizes the licensed spectrum when primary users are passive. Thus, the activities of primary radio (PR) and cognitive radio (CR) users play a major role in the performance of cognitive radio networks. In this paper, a joint solution for user modeling and spectrum management is offered. The activities of PR and CR users are modeled through threestate Markov chain as the spectrum resource blocks are utilized with help of servicelevel overbooking strategy. Simulation results show the performance of the booking limit with respect to the channel release ratio of CR users and consequently the improved revenue of the network.