Banavathu, N R and Khan, Mohammed Zafar Ali
(2018)
Optimization of N-out-of-K Rule.
PhD thesis, Indian institute of technology Hyderabad.
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Abstract
The explosive growth in wireless systems and services has led to spectrum
scarcity in wireless communications due to ever-increasing demand for
higher data rates. The shortage of useful radio spectrum is due to static
allocation to specific services and rigid regulation of the spectrum usage
rather than efficient utilization of the radio spectrum. Cognitive radio
(CR) technology has been proposed to alleviate the spectrum shortage
problem and the spectrum underutilization of current radio spectrum by
allowing the cognitive users (CUs) to access spectrum of the licensed or
primary user (PU) under sufficient protection to the PU. Therefore, spectrum
sensing is a fundamental component for the CU to accurately detect
the activities of the PU. However, spectrum sensing using single CU results
in poor detection performance due to mulipath and hidden terminal
problem. Therefore, cooperative spectrum sensing (CSS) is proposed to
enhance the detection accuracy by taking the advantage of spatial diversity
in the multiple CU observations. In this thesis, we consider decision
based CSS, where in each CU makes a binary decision on the activity of
the PU and the decisions are reported to the FC, where they are combined
using N-out-of-K rule, i.e., at least N CUs must favour for the presence
of the PU out of K CUs.
Most existing works in the literature on N-out-of-K rule are for homogeneous
CR networks, where CUs are assumed to have identical local false
alarm and identical detection probability. However, in practice, the sensing
accuracy will vary since the CUs are at different distances from the
PU. In this thesis, we consider heterogeneous CSS using the N-out-ofK
rule, wherein spatially dispersed CUs operate at different local false
alarm, detection probabilities and detect the activities of the PU using
the N-out-of-K rule. We obtain the generalized expressions for the global
false alarm and global detection probabilities for the N-out-of-K rule for
the heterogeneous CR network in the presence of control channel errors
and the proposed solution specializes to various existing results. We then
propose Neyman-Pearson (N-P) test which maximizes the global detection
probability of the N-out-of-K rule for a given target global false
alarm probability at the FC. We show limitations on the target global
false alarm probability due to control channel errors. We then consider
the optimization of N-out-of-K rule for the heterogeneous CR network
under Bayesian test and show that several existing works are special cases
of the proposed solution. We also obtain a most generalized expression
for optimal K for the homogeneous CR network and show that various
results are special cases of the proposed solution. A joint optimization
problem is formulated to optimize both N and K under Bayesian test.
The performance of the CSS obtained using joint optimized values of N
and K results in significant improvement.
Finally, we propose, optimization of N-out-of-K rule for maximizing the
average channel throughput (ACT) of the heterogeneous CR network. We
present asymptotic expressions for the optimal N and the optimal K by
maximizing the ACT under adequate protection to the PU. Note that the
mathematical results derived in this thesis are general and are applicable
to any detector used in CSS. However, we choose the energy detector (ED)
as an example to verify the theoretical findings
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