Fuzzy-logic-based channel selection in IEEE 802.22 WRAN

Joshi, Gyanendra Prasad; Acharya, Srijana; Kim, Sung Won

doi:10.1016/j.is.2014.05.009

Cited by 16 publications

(13 citation statements)

References 7 publications

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“…IEEE 802.22 wireless regional area network (WRAN) is proposed for the smart grid to allow unlicensed broadband network to opportunistically access the unused TV white space [30]. Fuzzy logic is also applied to the IEEE 802.22 to prioritize channels in the backup and candidate channel list [31]. Finally, fuzzy logic was also proposed for IEEE 802.22 WRAN in [32] to select channels based on required QoS using throughput, latency and reliability.…”

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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Section: Related Workmentioning

confidence: 99%

Fuzzy based Reliable Cooperative Spectrum Sensing for Smart Grid Environment

Nassef¹,

Alhebshi²

2020

IJACSA

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“…In [12], another scheme was proposed for channel selection by SUs in wireless regional area networks (WRANs) under the IEEE 802.22 standard. Therein, a backup channel and candidate channel list is created according to the previous transmission behavior of the PUs.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Fuzzy Logic Scheme for Efficient Channel Utilization in Cognitive Radio Networks

et al. 2019

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The proliferation of mobile devices and the heterogeneous environment of wireless communications have increased the need for additional spectrum for data transmission. It is not possible to altogether allocate a new band to all networks, which is why fully efficient use of the already available spectrum is the demand of the day. Cognitive radio (CR) technology is a promising solution for efficient spectrum utilization, where CR devices, or secondary users (SUs), can opportunistically exploit white spaces available in the licensed channels. SUs have to immediately vacate the licensed channel and switch to another available channel when they detect the arrival of the incumbent primary user. However, performance for the SU severely degrades if successive channel switching happens. Moreover, taking the channel-switching decisions based on crisp logic is not a suitable approach in the brain-empowered CR networks (CRNs) where sensing information is not only imprecise and inaccurate but also involves a major uncertainty factor. In this paper, we propose a fuzzy logic-based decision support system (FLB-DSS) that jointly deals with channel selection and channel switching to enhance the overall throughput of CRNs. The proposed scheme reduces the SU channel switching rate and makes channel selection more adaptable. The performance of the proposed scheme is evaluated using a Matlab simulator, and a comprehensive comparison study with a baseline scheme is presented. The simulation results are promising in terms of the throughput and the number of handoffs and making our proposed FLB-DSS a good candidate mechanism for SUs while making judicious decisions in the CR environment. INDEX TERMS Cognitive radio network, fuzzy logic, resource allocation, channel selection, handoff rate.

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“…To mitigate these challenges, the IEEE 802.22 standard specifies that a CR user should keep a candidate channel list (CCL) [17]. However, the IEEE 802.22 standard did not define a specific algorithm for selecting a target channel from the backup channel list (BCL), or for building a BCL from the CCL [18]. Similarly, to encourage novelty, the sorting/the ordering of the channels in the CCL is left open by the IEEE 802.22 standard.…”

Section: Introductionmentioning

confidence: 99%

An Effective Spectrum Handoff Based on Reinforcement Learning for Target Channel Selection in the Industrial Internet of Things

Oyewobi

Hancke

Abu‐Mahfouz

et al. 2019

Sensors

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The overcrowding of the wireless space has triggered a strict competition for scare network resources. Therefore, there is a need for a dynamic spectrum access (DSA) technique that will ensure fair allocation of the available network resources for diverse network elements competing for the network resources. Spectrum handoff (SH) is a DSA technique through which cognitive radio (CR) promises to provide effective channel utilization, fair resource allocation, as well as reliable and uninterrupted real-time connection. However, SH may consume extra network resources, increase latency, and degrade network performance if the spectrum sensing technique used is ineffective and the channel selection strategy (CSS) is poorly implemented. Therefore, it is necessary to develop an SH policy that holistically considers the implementation of effective CSS, and spectrum sensing technique, as well as minimizes communication delays. In this work, two reinforcement learning (RL) algorithms are integrated into the CSS to perform channel selection. The first algorithm is used to evaluate the channel future occupancy, whereas the second algorithm is used to determine the channel quality in order to sort and rank the channels in candidate channel list (CCL). A method of masking linearly dependent and useless state elements is implemented to improve the convergence of the learning. Our approach showed a significant reduction in terms of latency and a remarkable improvement in throughput performance in comparison to conventional approaches.

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Fuzzy-logic-based channel selection in IEEE 802.22 WRAN

Cited by 16 publications

References 7 publications

Fuzzy based Reliable Cooperative Spectrum Sensing for Smart Grid Environment

Fuzzy based Reliable Cooperative Spectrum Sensing for Smart Grid Environment

Hybrid Fuzzy Logic Scheme for Efficient Channel Utilization in Cognitive Radio Networks

An Effective Spectrum Handoff Based on Reinforcement Learning for Target Channel Selection in the Industrial Internet of Things

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