Strategic behaviour for spectrum access in hybrid overlay/underlay cognitive radio networks

Safwat, Mahammad A.; El-Badawy, Hesham M.

doi:10.1049/iet-com.2017.0898

Cited by 11 publications

(4 citation statements)

References 36 publications

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“…23 Moreover, the author has also emphasized the prediction errors, and pre-and post-fusion rules are investigated to minimize the errors. In Safwat and Elbadawy, 24 a game theory-based hybrid spectrum access scheme is proposed to maximize spectrum efficiency. The EE of traditional underlay, overlay, interweave schemes is estimated in Li et al 25 A hybrid spectrum access scheme is analyzed for a half duplexed and full duplexed spectrum sharing system to estimate EE loss.…”

Section: Prior Work and Motivationmentioning

confidence: 99%

Energy‐efficient framework for throughput enhancement of cognitive radio network

Bala

Ahuja

2021

Int J Communication

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Summary Recently, cognitive radio (CR) has evolved as a next‐generation communication technology that has the potential to use the radio frequency (RF) spectrum in most efficient manner as compared to the other existing cellular communication technologies. The performance of CR system largely depends upon the accuracy of the sensing results. However, the sensing ambiguities such as false alarm and miss detection cause spectrum underutilization and severe interference to the primary user (PU), respectively. In this paper, both issues are addressed by modifying the conventional frame structure by introducing two sensing slots along with a transmission slot to overcome sensing ambiguities. For the targeted probability of detection, the first sensing slot is kept small and fixed, and sensing results are stored in a flag bit up to that duration. The second sensing slot (optional) is used whenever the flag bit status varies in the current frame from the previous frame. Considering sensing throughput trade‐off issue of CR, the second sensing slot is optimized to enhance the effective throughput and energy efficiency of the secondary communication system. The simulation results are presented to validate the effectiveness of the proposed scheme with a modified frame structure, which is capable to achieve 65% higher energy efficiency than the conventional scheme when PU is present in a channel, that is, P(H1) = 1.

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Section: Prior Work and Motivationmentioning

confidence: 99%

Energy‐efficient framework for throughput enhancement of cognitive radio network

Bala

Ahuja

2021

Int J Communication

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“…Safwat and Elbadawy, 13 utilizing queuing model and match theory, discussed the decision issue of having connection to the hybrid overlay/underlay CR network. An observable queuing system is used to design the hybrid overlay/ underlay CR network.…”

Section: Related Work: a Brief Reviewmentioning

confidence: 99%

Cluster‐based spectrum access scheme selection and optimal relay link selection for hybrid overlay/underlay cognitive radio networks

Rajaganapathi

Nathan

2020

Int J Communication

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Cognitive radio (CR) network is the effective technique for the optimum utilization of the spectrum. The hybrid cognitive network (HCN) has been implemented to increase the utilization of the spectrum, by using both (overlay/underlay) modes of spectrum sharing. In overlay mode, cognitive radio (CR) opportunistically uses the spectrum, whereas in underlay mode, the CR uses the spectrum simultaneously with primary user (PU) with transmit power limitation. The HCN tries to use advantages of both modes of spectrum sharing. Its performance is based on the accuracy of the method used to find the PU status. Hence, in this paper, "clustering-based spectrum access scheme selection and optimal relay link selection for hybrid overlay/underlay cognitive radio networks" have been proposed to improve the performance of the hybrid cognitive radio network. In this proposed method, the scheme selection is done in two levels. In the first level, spectrum sharing scheme is selected based on the cluster in which the SU is present, and in the second level, scheme is selected based on the PU status. The cluster-based cooperative spectrum sensing method has been used to find the status of the PU. Compared with cooperative spectrum sensing, a cluster-based cooperative spectrum sensing has a huge effect on sensing accuracy. While the underlay scheme is used by SU, best relay link has been selected by using particle swarm optimization technique. Thus, the best scheme selection and relay selection improve the performance of hybrid cognitive radio network. The simulation result shows that the proposed method improves the throughput of the HCN.

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“…Cognitive radio technology allows cognitive users to access the primary user's authorized frequency band to increase the utilization of spectrum resources. The access modes of cognitive user can be mainly divided into overlay [8–10], underlay [11], and hybrid access mode [12]. Overlay access mode allows cognitive users to use authorized frequency bands not used by primary users.…”

Section: Introductionmentioning

confidence: 99%

Graph convolutional reinforcement learning for resource allocation in hybrid overlay–underlay cognitive radio network with network slicing

Yuan

Zhang

et al. 2022

IET Communications

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Nowadays, wireless communication system is facing the problems of spectrum resource shortage. Cognitive radio technology allows cognitive users to use the spectrums authorized to primary users to improve the spectrum utilization. In this paper, a cognitive network model based on hybrid overlay-underlay spectrum access mode is established. To solve the resource allocation problem, a multi-agent resource allocation algorithm based on graph convolution reinforcement learning which combines deep Q network (DQN) and graph attention network is proposed. DQN is used for action selection and graph attention network is used to obtain the information about neighbours, so as to achieve local cooperation. The proposed algorithm can adaptively optimize cognitive network throughput, spectrum efficiency, or power efficiency by controlling the transmission power and channel selection of cognitive users. To improve the information interaction efficiency, the agent's states are divided into two categories, whether it needs to interact with neighbours or not, which shortens training time and improves convergence speed. Simulation results show that the proposed algorithm can effectively improve the power efficiency of cognitive networks. Compared with Q-learning, DQN and exiting graph convolutional reinforcement learning algorithm, the proposed algorithm has faster convergence speed and higher stability, and obtains higher network power efficiency.

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Strategic behaviour for spectrum access in hybrid overlay/underlay cognitive radio networks

Cited by 11 publications

References 36 publications

Energy‐efficient framework for throughput enhancement of cognitive radio network

Energy‐efficient framework for throughput enhancement of cognitive radio network

Cluster‐based spectrum access scheme selection and optimal relay link selection for hybrid overlay/underlay cognitive radio networks

Graph convolutional reinforcement learning for resource allocation in hybrid overlay–underlay cognitive radio network with network slicing

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