Fast and Robust Spectrum Sensing for Cognitive Radio Enabled IoT

The development of spectral efficient solutions for internet of things (IoT) face challenges primarily due to the large-scale placement of an immense number of sensors and devices. Cognitive radio (CR) technology is considered as a potential solution to resolve the spectrum scarcity problems of IoT. Incorporation of CR in IoT encounters various challenges including fast response and efficient spectrum sensing even in low signal to noise ratio. In this study we integrate the basic functionalities of the both CR and IoT technology and present a five layered framework for CR enabled IoT. In addition to the framework we also proposed and develop a spectrum sensing algorithm for CR-based IoT architecture, meeting the efficiency and time sensitivity requirements. The proposed algorithm is more accurate, robust to noisy environment and four times faster than existing approaches. The developed algorithm is compared with existing blind spectrum sensing techniques in term of detection performance, optimization methods and computational complexity. Experimental evaluations with real wireless microphone signals demonstrate the effectiveness of the proposed scheme and show superiority over existing ones.

Automated summary

This study integrates CR and IoT technologies, presenting a five-layered framework for CR-enabled IoT and develops an efficient spectrum sensing algorithm that is more accurate, robust, and faster than existing approaches.