SDR-based Intelligent Cooperative Spectrum Sensing for Cognitive Radio Systems

Abstract

In this paper, the software and hardware of a software-defined radio (SDR) platform are used to implement and verify the blind real-time sensing act of intelligent collaborative spectrum sensing based on a new theoretical formula for constructing denoised mixed features named MSKU3 and paired with an unsupervised machine learning K-Medoids algorithm. Two low-cost RTL-SDR dongle hardware receivers are used as two cooperative unlicensed secondary users to capture the radio frequency of a licensed primary user channel. A host personal computer is used as a fusion center to run GNU-Radio software signal processing blocks to implement the developed method, and a single Universal Software Radio Peripheral (USRP) N210 hardware transmitter based on FPGA is used to take up unoccupied desired radio frequency bandwidth. Two scenarios of signal-to-noise ratio levels have been adopted to verify and test the sensing performance of the developed system. The first one occurs when unlicensed secondary users have equal signal-to-noise ratio values. The second occurs when unlicensed secondary users have different signal-to-noise ratio values since each secondary user has their location. The experimental results of detecting action in terms of the probability of detection for the proposed method show that the theoretical and practical results are very close to each other.