Real-coded genetic algorithm parameter setting for cognitive radio adaptation

Saoucha, Naziha Ali; Lammari, Amina; Benatchba, Karima

doi:10.1109/saconet.2013.6654574

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…The optimal sensing period to minimize the transmission delay is proposed by [10]. Authors of [11][12] proposed a scheme based on GA to choose the transmission parameters of the CR network. But they had not taken the sensing period and sensing schedule into consideration.…”

Section: Related Workmentioning

confidence: 99%

Sensing schedule optimization to minimize interference with primary users in cognitive radio network

DasMahapatra

Gandhi

Nair

et al. 2020

IJEECS

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<p class="Abstract">Radio Frequency (RF) spectrum, which is a limited resource, is facing the challenge of fulfilling the need of ever-growing users. To accommodate these users in an efficient manner, the idea of Cognitive Radio (CR) was proposed. It allows unlicensed users to use the licensed bands when the primary user is not using its band. To check the availability of the free spectrum, the system should be able to sense the RF environment around it, thus spectrum sensing becomes an important aspect of CR. In this work, the Genetic Algorithm (GA) is used to determine the periodic sensing schedule to maximize the number of transmission opportunities for unlicensed band users as well as to minimize interference with licensed users. This optimization scheme generates a maximum number of transmission opportunities with minimum interruption to the licensed user’s communication. The MATLAB software is used for simulation.</p>

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Section: Related Workmentioning

confidence: 99%

Sensing schedule optimization to minimize interference with primary users in cognitive radio network

DasMahapatra

Gandhi

Nair

et al. 2020

IJEECS

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show abstract

“…To optimise the expected transmission delay of secondary user traffic, they applied GA and they have validated their analysis & optimal access strategy design through various numerical results. Soucha et al (Soucha, Lamnari & Benatchba, 2013) applied GA to select the optimal transmission parameters and found satisfactory convergence speed. In 2013, Santos et al (Santos, Alves & Ferrira, 2013) proposed a hardware framework capable of efficiently supporting custom accelerators & applied to spectrum allocation problem in CRN.…”

Section: Application Of Soft Computingmentioning

confidence: 99%

Research on Soft Computing Techniques for Cognitive Radio

Mishra

Singh

et al. 2016

International Journal of Mobile Computing and Multimedia Communications

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Presently, the world of wireless communication is going under some crucial challenges, which attracts the attention of several researchers. Cognitive radio is defined as a multidimensional aware, autonomous radio system that learns from its experiences to reason, plan & decide future actions to meet user needs. Such a highly varied radio environment calls on intelligent management, allocation & usage of scarce resources. Issues like spectrum sensing & allocation, environmental learning i.e., adaptability & capability to learn attracts the attention of several soft computing learning & optimization techniques like neural networks, fuzzy logic, genetic algorithm & swarm intelligence. The cognitive engine behind the radio combines the sensing, learning, switching, and optimization algorithms to control & adapt the radio system from the physical layer to the top of the communication stack. This paper presents a critical review on different soft computing approaches applied over the cognitive radio issues & also points out different research directions over it.

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Real-coded genetic algorithm parameter setting for cognitive radio adaptation

Cited by 2 publications

References 11 publications

Sensing schedule optimization to minimize interference with primary users in cognitive radio network

Sensing schedule optimization to minimize interference with primary users in cognitive radio network

Research on Soft Computing Techniques for Cognitive Radio

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