| Summary: | With the deployment of new wireless communication devices and services, the demand for radio spectrum continues to grow. Spectrum utilization can he improved
using the Cognitive radio, concept which allows secondary users to opportunistically
access the unused licensed spectrum bands without causing undue interference to
licensed users. Most works on spectrum sensing assume a Gaussian noise model;
however, in some situations, an impulsive noise model may be more appropriate. In
this thesis, we consider the mixture Gaussian noise and the Laplacian noise model.
Approximate closed-form expressions for the probability density functions and
cumulative distribution functions of the output of an energy detector with Laplacian
noise were obtained using the Pearson approximation technique. An optimal
detection scheme based on the likelihood ratio test (I.RT) for mixture Gaussian and
Laplacian noise models was studied. Two sub-optimal algorithms, namely DFC detection
and EFC detection, are also evaluated. The results show that in contrast to
the Gaussian noise case, EFC detection does not always outperform DFC detection
and 1-out-of-N fusion rule does not always provide the lowest Pm for a given Pf
among K-out-of-N rules in a non-Gaussian noise environment. An algorithm, in
which large magnitude SU energy measurements are eliminated at the FC, is proposed
to improve the detection performance in impulsive noise, it is shown that
substantial detection performance can be achieved. In addition, we study a system
model in which the reporting channels between the SUs and the FC, and the channels
between any two SUs within the cluster experience Rayleigh fading. The results
show that in contrast to the Gaussian noise case, the cluster-based schemes do not
always outperform the conventional DFC detection. === Applied Science, Faculty of === Electrical and Computer Engineering, Department of === Graduate
|
|---|
