Geo-Location Information Aided Spectrum Sensing in Cellular Cognitive Radio Networks

Chen, Siji; Shen, Bin; Wang, Xin; Yoo, Sang‐Jo

doi:10.3390/s20010213

Cited by 7 publications

(5 citation statements)

References 37 publications

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“…Moreover, when PU is likely to leave the channel for a long time, SU may increase the period between two consecutive SS operations leading to economizing its energy and enhancing the spectral efficiency. The channel prediction may differ from a geo-location to another [ 109 ]. Accordingly, a mobile SU would switch from a channel to another based on its prediction of the PU behavior.…”

Section: Learning Techniques For Spectrum Sensingmentioning

confidence: 99%

Spectrum Sensing for Cognitive Radio: Recent Advances and Future Challenge

Nasser

Hassan

Chaaya

et al. 2021

Sensors

124

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Spectrum Sensing (SS) plays an essential role in Cognitive Radio (CR) networks to diagnose the availability of frequency resources. In this paper, we aim to provide an in-depth survey on the most recent advances in SS for CR. We start by explaining the Half-Duplex and Full-Duplex paradigms, while focusing on the operating modes in the Full-Duplex. A thorough discussion of Full-Duplex operation modes from collision and throughput points of view is presented. Then, we discuss the use of learning techniques in enhancing the SS performance considering both local and cooperative sensing scenarios. In addition, recent SS applications for CR-based Internet of Things and Wireless Sensors Networks are presented. Furthermore, we survey the latest achievements in Spectrum Sensing as a Service, where the Internet of Things or the Wireless Sensor Networks may play an essential role in providing the CR network with the SS data. We also discuss the utilisation of CR for the 5th Generation and Beyond and its possible role in frequency allocation. With the advancement of telecommunication technologies, additional features should be ensured by SS such as the ability to explore different available channels and free space for transmission. As such, we highlight important future research axes and challenging points in SS for CR based on the current and emerging techniques in wireless communications.

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Section: Learning Techniques For Spectrum Sensingmentioning

confidence: 99%

Spectrum Sensing for Cognitive Radio: Recent Advances and Future Challenge

Nasser

Hassan

Chaaya

et al. 2021

Sensors

124

View full text Add to dashboard Cite

show abstract

“…NNs must be properly built for the challenge; otherwise, performance may suffer; yet, when utilized correctly, they are a very effective tool for solving problems. 61,62 There are more forms of NNs than ANNs, such as convolutional neural networks (CNN), recurrent neural networks (RNN), and long short-term memory (LSTM). It will be fascinating to investigate their potential applications in CR.…”

Section: Future Scopementioning

confidence: 99%

Fuzzy logic, genetic algorithms, and artificial neural networks applied to cognitive radio networks: A review

Alkhayyat

Abedi

Bagwari

et al. 2022

International Journal of Distributed Sensor Networks

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Cognitive radios are expected to play an important role in capturing the constantly growing traffic interest on remote networks. To improve the usage of the radio range, a cognitive radio hub detects the weather, evaluates the open-air qualities, and then makes certain decisions and distributes the executives’ space assets. The cognitive radio works in tandem with artificial intelligence and artificial intelligence methodologies to provide a flexible and intelligent allocation for continuous production cycles. The purpose is to provide a single source of information in the form of a survey research to enable academics better understand how artificial intelligence methodologies, such as fuzzy logics, genetic algorithms, and artificial neural networks, are used to various cognitive radio systems. The various artificial intelligence approaches used in cognitive radio engines to improve cognition capabilities in cognitive radio networks are examined in this study. Computerized reasoning approaches, such as fuzzy logic, evolutionary algorithms, and artificial neural networks, are used in the writing audit. This topic also covers cognitive radio network implementation and the typical learning challenges that arise in cognitive radio systems.

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“…There are different ways for determining if other persons are nearby our position. It could be possible to use geolocation by GPS or 3G/4G for this purpose [15], [16]. However, this solution requires that the user whom we intend to locate has a mobile device and it would be necessary to enable the location of the terminal.…”

Section: Figure 1 Effects Of Selecting Different Switching Under Dynamic Conditionmentioning

confidence: 99%

A new system to detect coronavirus social distance violation

Botella-Campos

Gacía-Navas

Rego

et al. 2021

IJECE

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In this paper, a novel solution to avoid new infections is presented. Instead of tracing users’ locations, the presence of individuals is detected by analysing the voices, and people’s faces are detected by the camera. To do this, two different Android applications were implemented. The first one uses the camera to detect people’s faces whenever the user answers or performs a phone call. Firebase Platform will be used to detect faces captured by the camera and determine its size and estimate their distance to the phone terminal. The second application uses voice biometrics to differentiate the users’ voice from unknown speakers and creates a neural network model based on 5 samples of the user’s voice. This feature will only be activated whenever the user is surfing the Internet or using other applications to prevent undesired contacts. Currently, the patient’s tracking is performed by geolocation or by using Bluetooth connection. Although face detection and voice recognition are existing methods, this paper aims to use them and integrate both in a single device. Our application cannot violate privacy since it does not save the data used to carry out the detection and does not associate this data to people.

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Geo-Location Information Aided Spectrum Sensing in Cellular Cognitive Radio Networks

Cited by 7 publications

References 37 publications

Spectrum Sensing for Cognitive Radio: Recent Advances and Future Challenge

Spectrum Sensing for Cognitive Radio: Recent Advances and Future Challenge

Fuzzy logic, genetic algorithms, and artificial neural networks applied to cognitive radio networks: A review

A new system to detect coronavirus social distance violation

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