Performance analysis of spectrum sensing in cognitive radio at low SNR environment

Cognitive Radio is a promising technology to improve the spectrum exploitation and spectrum sensing is one of the key functionality for cognitive radio (CR) systems to operate in the available spectrum holes. To guard the primary users present in any spectrum from any interference, the CR should be able enough detect incumbent signals even at very low signal-to-noise ratio (SNR). In this paper, different spectrum sensing parameters like bit error probability (BEP), probability of detection, probability of false alarm are optimized under low SNR scenario using a demand and need based genetic algorithm(GA), taking the geographical variations of the spectrum holes in consideration. The results shows that the GA worked well and provides a better real life solution to the cognitive radio network.