Decision‐fusion‐based reliable CSS scheme in CR networks

Mustafa, Rashid; Jaglan, Reena Rathee; Agrawal, Sunil

doi:10.1049/iet-com.2018.5339

Cited by 10 publications

(8 citation statements)

References 37 publications

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“…2) Application of artificial neural networks at FC Apart from traditional classification methods, various types of artificial neural networks have been considered as promising tools for cooperative spectrum sensing improvements. In [107], three fusion methods were considered: conventional CSS model with hard fusion rules, ML-based fusion, and a cluster-based model. In the conventional fusion model, all SUs collect energy detection results, and send them to FC to evaluate global results using one of the rules: AND rule, OR rule, or majority rule.…”

Section: ) Classification Methods Applied At Fcmentioning

confidence: 99%

Artificial Intelligence for Radio Communication Context-Awareness

et al. 2021

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This paper surveys Artificial Intelligence (AI) methods for acquiring and managing contextof-operation awareness of radio communication nodes, links, and networks. The meaning and significance of context information and suitability of Machine Learning (ML) methods for the enrichment of context information is discussed. A number of context features are considered in this regard and thorough analysis on which ML methods are suitable to which part of context learning is provided. The added value of the paper is the presentation of a synthesized framework of context-information processing, sharing, and management in a radio communication network by delineating a network-embedded subsystem for this management. Recommendations for a future AI/ML-based radio communication system architectures are also provided.

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Section: ) Classification Methods Applied At Fcmentioning

confidence: 99%

Artificial Intelligence for Radio Communication Context-Awareness

et al. 2021

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“…This was done to reduce the high bandwidth requirement of common control channel. Rashid et al proposed a neural network‐based decision fusion scheme at FC, which is used to form a reliable decision about cooperative spectrum sensing. Neural network‐based system is used to learn previous decision values about the sensing.…”

Section: Channel Assignment In Literaturementioning

confidence: 99%

Cross‐layer perspective for channel assignment in cognitive radio networks: A survey

Gupta

Dhurandher

2019

Int J Communication

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Summary With the advent of various emerging wireless products, the usage of limited spectrum has grown exponentially in the recent years. In the next few years, that mobile data traffic globally is expected to grow up to 50 EB/month, which is nearly a five times increase over year 2018. Therefore, it will become extremely difficult to satisfy the ever increasing demand through the current fixed spectrum assignment policy in which spectrum band is exclusively used for the particular applications, and it has also led to underutilization of a significant portion of the spectrum (like TV bands). Cognitive radio networks has emerged as a possible solution for the problem which makes dynamic spectrum access possible for unlicensed user when licensed user is not active. Among various operations of cognitive radio, channel assignment to the unlicensed user is very important. Further, wireless regional area network is one of the most important application of cognitive radio, which provides wireless broadband to the rural area using vacant TV channels. This paper discusses channel assignment techniques considering various functionalities for cognitive radio networks in respect of wireless regional area network in the existing literature. Initially, a comprehensive introduction to both cognitive radio networks and wireless regional area networks is provided, and in the end, the paper summarizes the various issues and research challenges in the channel assignment for wireless regional area networks.

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“…The traffic statistics are determined by the application layer. 15 Neyman-Pearson criteria, an optimal soft combination scheme is then obtained. This scheme maximizes the detection probability rate for a false alarm.…”

Section: Introductionmentioning

confidence: 99%

Adaptive cluster‐based heuristic approach in cognitive radio networks for5Gapplications

Devaraj¹,

Aruna²,

Muthukumaran³

et al. 2021

Trans Emerging Tel Tech

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The main objective of cognitive radio network is to provide flexible spectrum management, by permitting the secondary users (SUs) to temporarily access the licensed spectrum in the absence of a primary user. In the existing backward induction approach, each SU performs spectrum sensing and consequently reports the sensing details to the centralized controller. In the proposed work, an adaptive cluster-based heuristic approach (ACBHA) with cooperative spectrum sensing (CSS) scheme has been proposed for cognitive radio medium access control networks for 5G applications. To identify the availability of unused spectrum, the proposed CSS-based ACBHA algorithm adopts a cluster-based selection scheme. Simulation results show that the proposed CSS-based ACBHA provides a better result compared with existing techniques. The performance parameters such as false alarm probability, the probability of detection, spectrum sensing time, slot length, noise density have been analyzed using MATLAB R2012a.

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Decision‐fusion‐based reliable CSS scheme in CR networks

Cited by 10 publications

References 37 publications

Artificial Intelligence for Radio Communication Context-Awareness

Artificial Intelligence for Radio Communication Context-Awareness

Cross‐layer perspective for channel assignment in cognitive radio networks: A survey

Adaptive cluster‐based heuristic approach in cognitive radio networks for5Gapplications

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