An unsigned autocorrelation based blind spectrum sensing approach for cognitive radio

A large part of unoccupied licensed spectrum can be vital for many applications and can be accessed opportunistically by Cognitive Radio. Cognitive Radio serves to be a framework for forthcoming 5G communication systems and therefore, extensive research is being carried out in its different domains. The task of sensing spectrum should be efficient and reliable to avoid interference between licensed and unlicensed user. A blind spectrum sensing scheme is proposed in this research which facilitates distinct discrimination between signal and noise components by using second order statistical characteristic. In this research, we have compared the results of our Autocorrelation based blind spectrum sensing scheme with Entropy based blind spectrum sensing scheme and Energy Detection scheme through Monte Carlo simulated graphs in AWGN channel. Simulations are carried out for a simple Frequency Modulated signal and 802.11a scenario. Threshold of our proposed scheme is independent of noise variance and thus, leads to optimal performance even in noisy environments than other two non-coherent methods with very few chances of false alarm.