Spectrum energy detection in cognitive radio networks based on a novel adaptive threshold energy detection method Abstract   Cognitive radio (CR) is a smart communication innovation in the development of the network. With advances in remote communications, the issue of bandwidth has proven to be more unique. The innovation of cognitive radio has turned as an approach to take care of this problem by enabling the unlicensed clients to utilize the authorized bands. A constrained available spectrum has been put in the way of impediments by the surpassing interest of remote presentations. To locate the unused spectrum in CR need one of the fundamental technique based on spectrum energy detection. The fundamental prerequisite for enabling CRs has been using the authorized spectrum for the auxiliary premise isnxe2x80x99t making impedance Primary Users (PU). Spectrum detection allows intelligent users to distinguish between unrecognized parts of the radio spectrum selfdetermination, and exempt the primary users from interrupting this way. Energy detection based spectrum detecting has been proposed for this work based on the Adaptive Threshold Spectrum Energy Detection (ATSED) strategy. By using ATSED the spectral detection performance can be significantly improved when the noise is uncertain and the primary user can reject the interference level. In this work, a model framework to describe and further Simulink investigation based on energy detection in a Matlab Simulink. The simulated resulting diagnostic effect on the probability is proves incremented basically when the signal sound rate (SNR) is to be increased. It is observed that the frequency detection is similar to a decrease in the frequency detection.

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.