Fuzzy Based Relay Selection for Secondary Transmission in Cooperative Cognitive Radio Networks

Banerjee, Jyoti Sekhar; Chakraborty, Arpita; Chattopadhyay, Abir

doi:10.1007/978-981-10-3908-9_34

Cited by 25 publications

(5 citation statements)

References 6 publications

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“…As a result of which no proper dataset is available to use as a reference. A synthetic multicriterion ( Banerjee, 2014 , Banerjee, 2015 , Banerjee, 2021 , Banerjee & Chakraborty, 2014 , Banerjee et al, 2017 , Banerjee et al, 2018b , Banerjee et al, 2018c , Banerjee et al, 2018a , Banerjee, Chakraborty, & Karmakar, 2013 , Banerjee & Karmakar, 2012 , Saha et al, 2019 , Saha et al, 2017 ) dataset has been coined by the authors to serve the purpose of this chapter, which is mentioned in the appendix ( Table A1 ). The attributes or symptoms and conditions that have been taken into consideration include age, body temperature, dry cough, chest pain, breathing rate, hypertension, cardiovascular diseases, diabetes, tiredness, current smoker, and contact with a person with fever or cold in the last few days.…”

Section: Proposed Covid-19 Detection Methodologymentioning

confidence: 99%

A new approach to predict COVID-19 using artificial neural networks

Guhathakurata¹,

Saha²,

Kundu

et al. 2022

Cyber-Physical Systems

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Section: Proposed Covid-19 Detection Methodologymentioning

confidence: 99%

A new approach to predict COVID-19 using artificial neural networks

Guhathakurata¹,

Saha²,

Kundu

et al. 2022

Cyber-Physical Systems

Self Cite

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“…Another type of strategy uses techniques based on logical statements. In [22], a collaborative decision-making process is proposed that uses fuzzy logic. The decision-making criterion works with relative link quality (RLQ) and SINR.…”

Section: Literature Reviewmentioning

confidence: 99%

Spectrum Decision-Making in Collaborative Cognitive Radio Networks

2020

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Spectral decision making is a function of the cognitive cycle. It aims to select spectral opportunities within a set of finite possibilities. Decision-making methodologies, based on collaborative information exchanges, are used to improve the selection process. For collaborative decision making to be efficient, decisions need to be analyzed based on the amount of information. Using little data can produce inefficient decisions, and taking a lot of data can result in high computational costs and delays. This document presents three contributions: the incorporation of a collaborative strategy for decision making, the implementation of real data, and the analysis of the amount of information through the number of failed handoffs. The collaborative model acts as a two-way information node, the information it coordinates corresponds to the Global System for Mobile Communications (GSM) band, and the amount of information to be shared is selected according to five levels of collaboration: 10%, 20%, 50%, 80%, and 100%, in which each percentage represents the total number of users that will be part of the process. The decision-making process is carried out by using two multi-criteria techniques: Feedback Fuzzy Analytical Hierarchical Process (FFAHP) and Simple Additive Weighting (SAW). The results are presented in two comparative analyses. The first performs the analysis using the number of failed handoffs. The second quantifies the level of collaboration for the number of failed handoffs. Based on the obtained percentage ratios, the information shared, and the average of increase rates, the level of collaboration that leads to efficient results is determined to be between 20% and 50% for the given number of failed handoffs.

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“…SUs can opt for the best channel levels of transmission based on the fuzzy logic decision support system. Research studies (Banerjee et al , 2017) discuss the fuzzy logic framework as a dynamic framework and as a possible solution for an interaction enabler that does not deprive PU quality amidst PUs and SUs transmissions. SINR and the RLQ have continuously used the signal inference process for managing input parameters for decision-making solutions based on fuzzy logic.…”

Section: Related Workmentioning

confidence: 99%

Fuzzy guided integrative factors-based spectrum decision-making in cognitive radio networks

S²,

Ramesh

2020

IJIUS

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PurposeElectronic devices aid communication during new communication phases and the scope of cognitive radio networks has changed communication paradigms through efficient use of spectrums. The communication prototype of cognitive radio networks defines user roles as primary user and secondary user in the context of the spectrum allocation and use. The users who have licensed authority of the spectrum are denoted as primary users, while other eligible users who access the corresponding spectrum are secondary users.Design/methodology/approachThe multiple factors of transmission service quality can have a negative influence due to improper scheduling of spectrum bands between primary users and secondary users. There are considerable contributions in contemporary literature concerning spectrum band scheduling under spectrum sensing. However, the majority of the scheduling models are intended to manage a limited number of transmission service quality factors. Moreover, these service quality factors are functional and derived algorithmically from the current corresponding spectrum. However, there is evidence of credible performance deficiency regarding contemporary spectrum sensing methodsFindingsThis article intends to portray a fuzzy guided integrated factors-based spectrum band sharing within the spectrum used by secondary users. This study attempts to explain the significance of this proposal compared to other contemporary models.Originality/valueThis article intends to portray a fuzzy guided integrated factors-based spectrum band sharing within the spectrum used by secondary users. This study attempts to explain the significance of this proposal compared to other contemporary models.

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Fuzzy Based Relay Selection for Secondary Transmission in Cooperative Cognitive Radio Networks

Cited by 25 publications

References 6 publications

A new approach to predict COVID-19 using artificial neural networks

A new approach to predict COVID-19 using artificial neural networks

Spectrum Decision-Making in Collaborative Cognitive Radio Networks

Fuzzy guided integrative factors-based spectrum decision-making in cognitive radio networks

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