Over the past few years, Cognitive Radio has become an important
research area in the field of wireless communications. It can play an
important role in dynamic spectrum management and interference
identification. There are many spectrum sensing techniques proposed
in literature for cognitive radio, but all those techniques detect only
presence or absence of the primary user in the designated band and
do not give any information about the used modulation scheme. In
certain applications, in cognitive radio receiver, it is necessary to
identify the modulation type of the signal so that the receiver
parameters can be adjusted accordingly. Most of the modulated
signals exhibit the property of Cyclostationarity that can be used for
the purpose of correct detection of primary user and the modulation
type. In this paper, we have proposed an enhanced signal detection
algorithm for cognitive radio receiver which makes use of
cyclostationarity property of the modulated signal to exactly detect,
the modulation type of the received signal using a trained neural
network. The algorithm gives better accuracy of signal detection even
in low SNR conditions. The use of a trained neural network makes it
more flexible and extendible for future applications.

plus-circle Add Review

comment

Reviews