Convergence of IoT and Cognitive Radio Networks: A Survey of Applications, Techniques, and Challenges

Khasawneh, Mahmoud; Azab, Ahmad; Alrabaee, Saed; Sakkal, Heba; Bakhit, Hossameldein Hussein

doi:10.1109/access.2023.3294091

Cited by 6 publications

(2 citation statements)

References 48 publications

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“…The secondary nonrelay nodes share data simultaneously with the active primary user in underlay mode. Cognitive radio networks can solve the spectrum scarcity issue in wireless communication systems [ 89 ], enabling dynamic opportunistic use by secondary users when primary users do not use the spectrum. Spectrum sensing facilitates the presence detection of primary users, while routing assists in coherent device communication, with both of them being pivotal in cognitive radio network IoT-based systems.…”

Section: Energy-harvesting Technology For Cognitive-radio-based Iot N...mentioning

confidence: 99%

Spectrum Sensing, Clustering Algorithms, and Energy-Harvesting Technology for Cognitive-Radio-Based Internet-of-Things Networks

Fernando,

Lăzăroiu

2023

Sensors

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The aim of this systematic review was to identify the correlations between spectrum sensing, clustering algorithms, and energy-harvesting technology for cognitive-radio-based internet of things (IoT) networks in terms of deep-learning-based, nonorthogonal, multiple-access techniques. The search results and screening procedures were configured with the use of a web-based Shiny app in the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) flow design. AMSTAR, DistillerSR, Eppi-Reviewer, PICO Portal, Rayyan, and ROBIS were the review software systems harnessed for screening and quality assessment, while bibliometric mapping (dimensions) and layout algorithms (VOSviewer) configured data visualization and analysis. Cognitive radio is pivotal in the utilization of an adequate radio spectrum source, with spectrum sensing optimizing cognitive radio network operations, opportunistic spectrum access and sensing able to boost the efficiency of cognitive radio networks, and cooperative spectrum sharing together with simultaneous wireless information and power transfer able increase spectrum and energy efficiency in 6G wireless communication networks and across IoT devices for efficient data exchange.

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Section: Energy-harvesting Technology For Cognitive-radio-based Iot N...mentioning

confidence: 99%

Spectrum Sensing, Clustering Algorithms, and Energy-Harvesting Technology for Cognitive-Radio-Based Internet-of-Things Networks

Fernando,

Lăzăroiu

2023

Sensors

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“…The relationship between spectrum sensing, clustering algorithms, and energy‐harvesting technologies for cognitive‐radio‐based IoT networks is investigated in terms of deep‐learning‐based, nonorthogonal, multiple‐access strategies in Fernando and Lăzăroiu 23 . Recent research and advancements in CR network based IoT systems have been investigated, as well as evaluated in Khasawneh et al 24 where the authors discuss about the sensing and routing protocols in CR networks linked with the IoT. Gupta and Prakriya 25 investigate the uplink's throughput performance with NOMA/OMA switching, ideal user selection, and channel state information (CSI)‐based interference temperature limit (ITL) portioning, accounting for SIC errors.…”

Section: Introductionmentioning

confidence: 99%

Outage of cooperative NOMA with an energy harvesting relay in an underlay cognitive radio network

Ghosh,

Dhar Roy,

Kundu

2024

Int J Communication

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SummaryThis paper analyzes the performance of a cooperative nonorthogonal multiple access (NOMA) in an underlay cognitive radio network aided by an energy harvesting relay. A secondary source transmits signal for two users, where a near user acts as a relay for the far user. The far user applies the selection combining (SC) approach on the signals which were relayed by the near user and received via direct path from the secondary source. We analytically derive the outage probability (OP) of each user separately, the overall system OP, and the throughput of the system. The impact of the power allocation coefficient of NOMA and energy harvesting parameters on outage is indicated. Further, the performance of the network is investigated with imperfection in successive interference cancellation (SIC), maximal ratio combining (MRC) at relay, and Nakagami‐m fading. The results derived analytically are supported by simulation in MATLAB.

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Transient analysis of enhanced hybrid spectrum access for QoS provisioning in multi-class cognitive radio networks

Kulshrestha,

Goel,

Balhara

2024

Wireless Netw

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Convergence of IoT and Cognitive Radio Networks: A Survey of Applications, Techniques, and Challenges

Cited by 6 publications

References 48 publications

Spectrum Sensing, Clustering Algorithms, and Energy-Harvesting Technology for Cognitive-Radio-Based Internet-of-Things Networks

Spectrum Sensing, Clustering Algorithms, and Energy-Harvesting Technology for Cognitive-Radio-Based Internet-of-Things Networks

Outage of cooperative NOMA with an energy harvesting relay in an underlay cognitive radio network

Transient analysis of enhanced hybrid spectrum access for QoS provisioning in multi-class cognitive radio networks

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