Evaluation of blind sensing algorithms in the 2.4 GHz ISM-band on GNU radio and USRP2

Cognitive Radio (CR) is known as a promising and effective technology to improve radio spectrum utilization. The main challenge for cognitive radio systems is to detect the existence of other users reliably in order to minimize interference. Hence, spectrum sensing is one of the most important and most challenging issues in cognitive radio systems. Different spectrum sensing algorithms have been discussed theoretically and evaluated by simulations in literature. In this paper, a practical application of three different spectrum sensing algorithms is presented: energy detection, prolate filter bank and statistical covariance. The performance of these algorithms was evaluated on GNU Radio platform with USRP2 in the license-free 2,4 GHz ISM Band. An IEEE 802.15.1 signal was used for the performance analysis. The spectrum sensing algorithms were evaluated for detection accuracy and computational costs. Both of these parameters are vital for the practical application of spectrum sensing algorithms in cognitive radio systems.