Survey on spectrum sharing/allocation for cognitive radio networks Internet of Things

Tarek, Dina; Benslimane, Abderrahim; Darwish, M.; Kotb, Amira

doi:10.1016/j.eij.2020.02.003

Cited by 40 publications

(18 citation statements)

References 21 publications

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“…Resource allocation in cognitive radio networks has been widely studied, [18,6] summarizes these existing studies. The main optimization objectives of resource allocation in cognitive radio network include maximizing throughput, spectrum efficiency and energy efficiency, minimizing interference and ensuring the quality of service of users.…”

Section: Related Workmentioning

confidence: 99%

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Deep reinforcement learning for resource allocation with network slicing in cognitive radio network

Yuan

Zhang

Qie

et al. 2021

ComSIS

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With the development of wireless communication technology, the requirement for data rate is growing rapidly. Mobile communication system faces the problem of shortage of spectrum resources. Cognitive radio technology allows secondary users to use the frequencies authorized to the primary user with the permission of the primary user, which can effectively improve the utilization of spectrum resources. In this article, we establish a cognitive network model based on under1 lay model and propose a cognitive network resource allocation algorithm based on DDQN (Double Deep Q Network). The algorithm jointly optimizes the spectrum efficiency of the cognitive network and QoE (Quality of Experience) of cognitive users through channel selection and power control of the cognitive users. Simulation results show that proposed algorithm can effectively improve the spectral efficiency and QoE. Compared with Q-learning and DQN, this algorithm can converge faster and obtain higher spectral efficiency and QoE. The algorithm shows a more stable and efficient performance.

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Section: Related Workmentioning

confidence: 99%

“…Combining the definition of the action value function and the cumulative discount reward function, we can obtain the Bellman equation form of the action value function through a similar derivation process, as shown in ( 17). ( 18) and ( 19) are Bellman optimality equations.…”

Section: Reinforcement Learningmentioning

confidence: 99%

Deep reinforcement learning for resource allocation with network slicing in cognitive radio network

Yuan

Zhang

Qie

et al. 2021

ComSIS

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“…The increasing demand for more spectrum has motivated the development of CR based network to solve both spectrum scarcity and reliability problems facing the introduction of a wide range of new applications [14]. However, most of the previous work on spectrum sensing have assumed simplified propagation model in which received signal depends only on distance between transmitter and receiver.…”

Section: Related Workmentioning

confidence: 99%

Fuzzy based Reliable Cooperative Spectrum Sensing for Smart Grid Environment

Nassef¹,

Alhebshi²

2020

IJACSA

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The huge demand for spectrum has created immediate need to make available new licensed and/or unlicensed spectrum bands to satisfy the explosive growth of spectrum demands and to satisfy the quality of service requirements of diverse applications. Spectrum shortage and harsh environment have become a challenging bottleneck to achieve reliable communications in the smart grid. Cognitive radio is the emerging technology to achieve both spectrum and reliability awareness. Cooperative spectrum sensing takes advantage of spatial diversity to reduce the impact of receiver uncertainty. However, the harsh smart grid environments limit advantageous of cooperation due to variations of signal to noise ratio on which energy detection technique depends on. This paper proposes a reliable spectrum detection for a cluster based cooperative spectrum sensing in harsh smart grid environment, where cognitive cluster heads and a centralized cognitive radio based fusion center are deployed to solve both spectrum and reliability problems. The proposed fuzzy inference system is based on three fuzzy descriptors of energy difference, link quality, and local probability of detection. The results show the superiority of proposed fuzzy based fusion scheme to enhance accuracy of spectrum decision in harsh environment.

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“…In addition, the results are used in the analysis of the performance of a cooperative cognitive network. A large number of recent works in the literature addresses the use of cooperative cognitive networks 28‐31 . The one considered here is based on the system model presented in Pereira et al 32 and is detailed later in this manuscript.…”

Section: Introductionmentioning

confidence: 99%

Approximations for the product, ratio, and sum ofα‐μrandom variables with application in the analysis of cognitive radio networks

Leonardo

Mafra

Montejo‐Sánchez

et al. 2021

Int J Communication

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Summary Novel methods to approximate the probability density function of the product, ratio, and sum of α‐μ random variables are presented in this article. The approximations are simple to compute and produce results that range from fairly accurate to perfectly accurate, depending on the parameters of the involved α‐μ distributions. In addition, as an application of the results, it is investigated the performance of a cooperative cognitive network in which the secondary source and the relays are energy‐constrained nodes and harvest their energy from the primary network.

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Survey on spectrum sharing/allocation for cognitive radio networks Internet of Things

Cited by 40 publications

References 21 publications

Deep reinforcement learning for resource allocation with network slicing in cognitive radio network

Deep reinforcement learning for resource allocation with network slicing in cognitive radio network

Fuzzy based Reliable Cooperative Spectrum Sensing for Smart Grid Environment

Approximations for the product, ratio, and sum ofα‐μrandom variables with application in the analysis of cognitive radio networks

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