Abstract:
The unstoppable evolution that has affected mobile telecommunication systems in the
last three decades has caused the occupation of the licensed frequencies, but at the
same time these frequencies are not being used efficiently. Cognitive Radio is the key
technology introduced to overcome the main problems of the spectrum utilization,
since it offers the opportunity for other unlicensed users to utilize the licensed band
while it is not being used by primary user. Even though it increases the efficiency of
spectrum utilization, spectrum sensing in cognitive radios still faces problems for
higher-performance and more energy-efficient systems. In this work, are taken in
consideration two machine learning algorithms as decision-making tools in the fusion
centre of cooperative spectrum sensing network based on energy detection technique.
The effectivity of these algorithms is evaluated using Receiver Operating
Characteristics (ROC) curve and Area Under The Curve (AUC) values, considering
seperately additive white Gaussian noise and Rayleigh fading channel. Moreover, the
training period of each algorithm is analyzed to evaluate the execution cost for each of
them.
