Spectrum mobility in cognitive radio networks

2012 ◽  
Vol 50 (6) ◽  
pp. 114-121 ◽  
Author(s):  
Ivan Christian ◽  
Sangman Moh ◽  
Ilyong Chung ◽  
Jinyi Lee
Keyword(s):  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Radio Networks ◽  
Spectrum Mobility

Sensing Techniques for Next Generation Cognitive Radio Networks

2019 ◽  
pp. 108-124
Author(s):  
Dhaya R. ◽  
Rajeswari A. ◽  
Kanthavel R.
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Spectrum Sharing ◽  
Spectrum Management ◽  
Radio Networks ◽  
Maximum Throughput ◽  
Spectrum Mobility ◽  
Static Allocation ◽  
Spectrum Holes

Cognitive radio is the technology used to solve the problem of spectrum underutilization by performing spectrum sensing, spectrum management, spectrum sharing, and spectrum mobility. The primary goal of cognitive radio is open spectrum sharing. Spectrum is a scarce and valuable natural resource that has to be used very effectively. The static allocation of spectrum to the licensed users will lead to wastage of resources when the spectrum is unused by the licensed user. Spectrum sensing methodology helps in detecting the spectrum holes and enables the unlicensed users to access the unused bands in the licensed spectrum effectively without interfering the licensed users. Cognitive thinking takes wireless communication to the next level by sensing the electromagnetic environment and dynamically adjusts its operating parameters in order to achieve maximum throughput, mitigate interference, facilitate interoperability, etc. The chapter presents the basics of cognitive radio networks, its architecture, its application, and advantages of cognitive radio networks.

scholarly journals Modelling of Spectrum Handover Scheme using Particle Swarm Intelligence in Cognitive-radio Networks

2019 ◽  
Vol 8 (2) ◽  
pp. 3331-3336
Keyword(s):  
Cognitive Radio ◽  
Swarm Intelligence ◽  
Cognitive Radio Networks ◽  
Service Time ◽  
Research Work ◽  
Particle Swarm ◽  
Radio Networks ◽  
Particle Swarm Intelligence ◽  
Spectrum Mobility ◽  
Swarm Size

Cognitive-radio is a self-adaptive network technology, which helps in detecting idle channels within a spectrum range. Cognitive radio has four functionalities namely Spectrum sensing, decision, sharing and mobility. This research work is in the domain of spectrum mobility. Spectrum mobility deals with motion of unlicensed users in the network. Unlicensed users are the unauthorized users of cognitive radio networks who have lower priority than licensed ones. Major functionality of spectrum mobility is spectrum handover and connection management. In cognitive-radio, the method of switching channels is termed as spectrum handover. Whenever a high priority user appears to occupy its spectrum band, that is already been utilized by a low priority user, spectrum handover takes place. During this process, a lot of handover delay occurs, which results in increasing the total service time of transmission. Total service time of spectrum handover means amount of time required to perform successful handover during spectrum mobility stage in cognitive-radio-networks. To decrease this total service time of spectrum handover we have utilized the concept of Particle Swarm Intelligence and M/G/1 queuing model. The parameters used for the purpose are swarm size, arrival rate, service rate, acceleration coefficients, processing time and channel switching time. Swarm size indicates the number of particles present in a swarm. In this research work, value of swarm size is varied to see its effect on total service time of spectrum handover process. Numerical results demonstrate that by increasing the value of swarm size, total service time decreases.

scholarly journals SVM-Based Spectrum Mobility Prediction Scheme in Mobile Cognitive Radio Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Yao Wang ◽  
Zhongzhao Zhang ◽  
Lin Ma ◽  
Jiamei Chen
Keyword(s):  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
High Speed ◽  
Radio Networks ◽  
Support Vector ◽  
Mobility Prediction ◽  
Prediction Scheme ◽  
Spectrum Mobility ◽  
Short Time

Spectrum mobility as an essential issue has not been fully investigated in mobile cognitive radio networks (CRNs). In this paper, a novel support vector machine based spectrum mobility prediction (SVM-SMP) scheme is presented considering time-varying and space-varying characteristics simultaneously in mobile CRNs. The mobility of cognitive users (CUs) and the working activities of primary users (PUs) are analyzed in theory. And a joint feature vector extraction (JFVE) method is proposed based on the theoretical analysis. Then spectrum mobility prediction is executed through the classification of SVM with a fast convergence speed. Numerical results validate that SVM-SMP gains better short-time prediction accuracy rate and miss prediction rate performance than the two algorithms just depending on the location and speed information. Additionally, a rational parameter design can remedy the prediction performance degradation caused by high speed SUs with strong randomness movements.

scholarly journals Ingenious Method for Conducive Handoff Appliance in Cognitive Radio Networks

2018 ◽  
Vol 8 (6) ◽  
pp. 5195 ◽  
Author(s):  
J. Josephine Dhivya ◽  
M. Ramaswami
Keyword(s):  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Cuckoo Search ◽  
Mobile Node ◽  
Radio Networks ◽  
Link Failure ◽  
Fitness Value ◽  
Spectrum Mobility ◽  
Overall Performance ◽  
Ingenious Method

Wireless communications deployed in the current epoch claims ceaseless connection among its users thereby leading to the investigation of Cognitive Radio Networks (CRN) which enables to make use of unallocated spectrum optimally and provides uninterrupted connection. Establishing interminable connectivity during the handoff process in spectrum mobility of CRN is a challenging task. This paper elucidates the optimization of handoff process carried out in CRN by incorporating an intelligent method. This includes fuzzy logic wherein the handoff parameters are processed thereby indicating the need of handoff. The proffered method also comprises of a part of genetic algorithm which yields fitness value for reducing the handoff occurrences and enhancing the overall performance of the system is promoted using cuckoo search which decides the mobile node from which the handoff process has to initiate based on the priority generated. This technique ensures that decision is taken ahead of link failure rather than range failure which are the key point in comparison to the existing system. Results obtained through the simulation are satisfactory in terms of delay, throughput, number of failed handoff and handoffs performed in comparison to the existing fuzzy based handoff process in CRN.

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