Implementation and Analysis of Spectrum Sensing Algorithms for SIMO Links

Cognitive radio is an autonomous transceiver that is continuously sensing the ongoing communication in its environment, it then starts the communication whenever it is appropriate. Therefore, cognitive radio helps improving the spectrum utilization of the overall communication system. However, without suitable spectrum sensing techniques, cognitive radio would fail. Hence, in this thesis we investigate and implement various spectrum sensing algorithms via software defined radio for both single antenna and multiple antenna cases. The main communication scheme that we are using is OFDM. Moreover, both computer simulations and real-world measurements, have also been done for comparison and analysis of the detector’s performance. The detectors we are using are based on correlation function of the received signal and generalized likelihood ratio test with its eigenvalue. The results from the simulations and measurements are then represented as probability of missed detection curves for different signal to noise ratios. Our results show that the performance of the generalized likelihood ratio test based detectors are at least 2 dB better than the correlation based detector in our measurement. Moreover, our simulations show that they are able to outperform the correlation function detector by more than 6 dB. Although, generalized likelihood ratio test based detectors seem to be better than the correlation function based detector, it requires more computational power which may limit its practical use.