Signals Classification and Identification for Cognitive Radio

Abstract

From the past few decades the need for higher data rates in wireless communication has increased exponentially. The spectrum access policy has restricted the growing demand of wireless devices. Out of the total given spectrum only a small portion is given for open user and a large part is given for licensed user. But the unlicensed spectrum is used more than the licensed spectrum, which forced the FCC to design a policy so that the limited spectrum can be used efficiently. The spectral occupancy of licensed spectrum is very less as compared to the unlicensed spectrum. Cognitive radio has emerged as a solution for this inefficient utilization of licensed spectrum; it identifies the unused portion of licensed spectrum which is called white space and makes them available for unlicensed user. Before giving the white space to the secondary user for transmitting the signals, it is required to identifies and classify the signals, so that the cognitive radio can work efficiently. so to classify the incoming signals many method are used like feature extraction method and neural network method.in feature extraction method, first we have to find out the feature value from all the signals then by comparing that with the threshold value we can find out the modulation type of the signal.In neural network method, we have to give the feature value to a neural network and that network will find out the type of the signal