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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
Cluster-based cooperative subcarrier sensing using antenna diversity-based weighted data fusion
Cooperative spectrum sensing (CSS) is used in cognitive radio (CR) networks to improve the spectrum sensing performance in shadow fading environments. Moreover, clustering in CR networks is used to reduce reporting time and bandwidth overhead during CSS. Thus, cluster-based cooperative spectrum sensing (CBCSS) has manifested satisfactory spectrum sensing results in harsh environments under processing constraints. On the other hand, the antenna diversity of multiple input multiple output CR systems can be exploited to further improve the spectrum sensing performance. This paper presents the CBCSS performance in a CR network which is comprised of single- as well as multiple-antenna CR systems. We give theoretical analysis of CBCSS for orthogonal frequency division multiplexing signal sensing and propose a novel fusion scheme at the fusion center which takes into account the receiver antenna diversity of the CRs present in the network. We introduce the concept of weighted data fusion in which the sensing results of different CRs are weighted proportional to the number of receiving antennas they are equipped with. Thus, the receiver diversity is used to the advantage of improving spectrum sensing performance in a CR cluster. Simulation results show that the proposed scheme outperforms the conventional CBCSS scheme
Multiband Spectrum Access: Great Promises for Future Cognitive Radio Networks
Cognitive radio has been widely considered as one of the prominent solutions
to tackle the spectrum scarcity. While the majority of existing research has
focused on single-band cognitive radio, multiband cognitive radio represents
great promises towards implementing efficient cognitive networks compared to
single-based networks. Multiband cognitive radio networks (MB-CRNs) are
expected to significantly enhance the network's throughput and provide better
channel maintenance by reducing handoff frequency. Nevertheless, the wideband
front-end and the multiband spectrum access impose a number of challenges yet
to overcome. This paper provides an in-depth analysis on the recent
advancements in multiband spectrum sensing techniques, their limitations, and
possible future directions to improve them. We study cooperative communications
for MB-CRNs to tackle a fundamental limit on diversity and sampling. We also
investigate several limits and tradeoffs of various design parameters for
MB-CRNs. In addition, we explore the key MB-CRNs performance metrics that
differ from the conventional metrics used for single-band based networks.Comment: 22 pages, 13 figures; published in the Proceedings of the IEEE
Journal, Special Issue on Future Radio Spectrum Access, March 201
Analytic solutions to a Marcum Q-function-based integral and application in energy detection of unknown signals over multipath fading channels
This work presents analytic solutions for a useful integral in wireless
communications, which involves the Marcum function in combination with an
exponential function and arbitrary power terms. The derived expressions have a
rather simple algebraic representation which renders them convenient both
analytically and computationally. Furthermore, they can be useful in wireless
communications and particularly in the context of cognitive radio
communications and radar systems, where this integral is often encountered. To
this end, we derive novel expressions for the probability of detection in
energy detection based spectrum sensing over fading channels.
These expressions are given in closed-form and are subsequently employed in
analyzing the effects of generalised multipath fading conditions in cognitive
radio systems. As expected, it is shown that the detector is highly dependent
upon the severity of fading conditions as even slight variation of the fading
parameters affect the corresponding performance significantly.Comment: Latest/Preprint Versio
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