Spectrum Sensing, Clustering Algorithms, and Energy-Harvesting Technology for Cognitive-Radio-Based...

Area of Science:

• Wireless Communication
• Internet of Things (IoT)
• Cognitive Radio Networks

Background:

• Cognitive radio is crucial for efficient spectrum utilization.
• Spectrum sensing optimizes cognitive radio network operations.
• Energy harvesting is vital for sustainable IoT networks.

Purpose of the Study:

• To identify correlations between spectrum sensing, clustering algorithms, and energy harvesting in cognitive radio IoT networks.
• To analyze these correlations in the context of deep-learning-based, nonorthogonal, multiple-access techniques.
• To provide insights for 6G wireless communication networks and IoT devices.

Main Methods:

• Systematic review following Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines.
• Utilized web-based Shiny app for search and screening.
• Employed review software (AMSTAR, DistillerSR, etc.) for quality assessment.
• Applied bibliometric mapping (Dimensions) and layout algorithms (VOSviewer) for data visualization.

Main Results:

• Spectrum sensing optimizes cognitive radio network performance.
• Cooperative spectrum sharing enhances spectrum and energy efficiency.
• Simultaneous wireless information and power transfer (SWIPT) is key for IoT devices.

Conclusions:

• Integrating spectrum sensing, clustering, and energy harvesting is essential for efficient cognitive radio IoT networks.
• These technologies collectively improve spectrum and energy efficiency in 6G.
• Further research can optimize deep-learning-based nonorthogonal multiple access for these networks.