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Performance analysis of energy detector over generalised wireless channels in cognitive radio
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London.This thesis extensively analyses the performance of an energy detector which is
widely employed to perform spectrum sensing in cognitive radio over different generalised
channel models. In this analysis, both the average probability of detection and
the average area under the receiver operating characteristic curve (AUC) are derived
using the probability density function of the received instantaneous signal to noise
ratio (SNR). The performance of energy detector over an ŋ --- µ fading, which is used
to model the Non-line-of-sight (NLoS) communication scenarios is provided. Then,
the behaviour of the energy detector over к --- µ shadowed fading channel, which is
a composite of generalized multipath/shadowing fading channel to model the lineof-
sight (LoS) communication medium is investigated. The analysis of the energy
detector over both ŋ --- µ and к --- µ shadowed fading channels are then extended to
include maximal ratio combining (MRC), square law combining (SLC) and square
law selection (SLS) with independent and non-identically (i:n:d) diversity branches.
To overcome the problem of mathematical intractability in analysing the energy
detector over i:n:d composite fading channels with MRC and selection combining
(SC), two different unified statistical properties models for the sum and the maximum
of mixture gamma (MG) variates are derived. The first model is limited by the value
of the shadowing severity index, which should be an integer number and has been
employed to study the performance of energy detector over composite α --- µ /gamma
fading channel. This channel is proposed to represent the non-linear prorogation
environment. On the other side, the second model is general and has been utilised to
analyse the behaviour of energy detector over composite ŋ --- µ /gamma fading channel.
Finally, a special filter-bank transform which is called slantlet packet transform
(SPT) is developed and used to estimate the uncertain noise power. Moreover, signal
denoising based on hybrid slantlet transform (HST) is employed to reduce the noise
impact on the performance of energy detector. The combined SPT-HST approach
improves the detection capability of energy detector with 97% and reduces the total
computational complexity by nearly 19% in comparison with previously implemented
work using filter-bank transforms. The aforementioned percentages are measured at
specific SNR, number of selected samples and levels of signal decompositionMartyrs Foundatio
Entropy and Energy Detection-based Spectrum Sensing over F Composite Fading Channels
In this paper, we investigate the performance of energy detection-based
spectrum sensing over F composite fading channels. To this end, an analytical
expression for the average detection probability is firstly derived. This
expression is then extended to account for collaborative spectrum sensing,
square-law selection diversity reception and noise power uncertainty. The
corresponding receiver operating characteristics (ROC) are analyzed for
different conditions of the average signal-to-noise ratio (SNR), noise power
uncertainty, time-bandwidth product, multipath fading, shadowing, number of
diversity branches and number of collaborating users. It is shown that the
energy detection performance is sensitive to the severity of the multipath
fading and amount of shadowing, whereby even small variations in either of
these physical phenomena can significantly impact the detection probability. As
a figure of merit to evaluate the detection performance, the area under the ROC
curve (AUC) is derived and evaluated for different multipath fading and
shadowing conditions. Closed-form expressions for the Shannon entropy and cross
entropy are also formulated and assessed for different average SNR, multipath
fading and shadowing conditions. Then the relationship between the Shannon
entropy and ROC/AUC is examined where it is found that the average number of
bits required for encoding a signal becomes small (i.e., low Shannon entropy)
when the detection probability is high or when the AUC is large. The difference
between composite and traditional small-scale fading is emphasized by comparing
the cross entropy for Rayleigh and Nakagami-m fading. A validation of the
analytical results is provided through a careful comparison with the results of
some simulations.Comment: 30 pages, 11 figures, 1 table, Submitted to IEEE TCO
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