Spectrum Sensing for Cognitive Radio: Recent Advances and Future Challenge

Area of Science:

• Electrical Engineering
• Computer Science
• Telecommunications

Background:

• Spectrum Sensing (SS) is crucial for Cognitive Radio (CR) networks to identify available frequency resources.
• Understanding Half-Duplex and Full-Duplex paradigms is essential for optimizing CR network performance.

Purpose of the Study:

• To provide a comprehensive survey of recent advancements in Spectrum Sensing for Cognitive Radio networks.
• To analyze Full-Duplex operation modes, learning techniques, and emerging applications in CR.

Main Methods:

• Review and synthesis of current literature on Spectrum Sensing in Cognitive Radio.
• Discussion of Full-Duplex operation modes, focusing on collision and throughput.
• Exploration of machine learning applications in local and cooperative sensing scenarios.

Main Results:

• Detailed analysis of Full-Duplex modes and their impact on CR network efficiency.
• Assessment of learning techniques for enhancing SS performance in various sensing scenarios.
• Overview of emerging SS applications in CR-based Internet of Things (IoT) and Wireless Sensor Networks (WSNs).

Conclusions:

• Spectrum Sensing is evolving with new techniques and applications, including its role in 5G and beyond.
• Future research should focus on advanced SS features for dynamic channel exploration and efficient frequency allocation.
• Integration of IoT and WSNs offers new possibilities for Spectrum Sensing as a Service.