Abstract:
Cognitive Radio has been invented to provide wireless communications with
efficient radio spectrum utilization. The secondary users (SUs) can therefore,
access opportunistically licensed band by sensing spectrum holes without interfering
with primary users (PU) or keeping the interference, if it happens below
a tolerable threshold. Main functions of cognitive radio are: Spectrum sensing,
spectrum management, spectrum mobility and spectrum sharing. This research
focuses on sensing and spectrum access optimization in cooperative multi-hop
cognitive radio networks. In a cooperative spectrum sensing, nodes located in
their vicinities can experience spatially correlated fading and it leads to a degraded
detection performance. To combat that effect, it is has been demonstrated
in several works that by selecting only spatially independent nodes, good
results can be obtained. A fuzzy-based user selection is investigated to cope with
the aforementioned issues in a multi-hop clustered cooperative spectrum sensing
architecture. Moreover, many researchers have based their work on single-hop
cognitive radio network architecture. However, this architecture does not portray
practical environments whereby nodes can be located far from each other and will
hence need their communication to be forwarded through relays. By optimizing
fuzzy inputs, we are able to achieve a high detection performance in a multi-hop
architecture by selecting only less correlated users to cooperate. By considering
uncorrelated users individually, the developed fuzzy based detection system outperforms
the distance based one by providing probability of detection 40% more
than the distance-based one when the decorrelation distance is 30 meters. On
the other hand, when the decorrelation distance takes respectively values of 65m
and 100m, the system does not show any gain compared to the distance-based
one. Finally, when using only uncorrelated users, the detection performance is
approximately the double of the one when using correlated users.
