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|Title: ||Collaborative spectrum sensing in cognitive radio networks|
|Authors: ||Sun, Hongjian|
|Supervisor(s): ||Laurenson, David I.|
Thompson, John S.
|Issue Date: ||2011|
|Publisher: ||The University of Edinburgh|
|Abstract: ||The radio frequency (RF) spectrum is a scarce natural resource, currently regulated by government
agencies. With the explosive emergence of wireless applications, the demands for the
RF spectrum are constantly increasing. On the other hand, it has been reported that localised
temporal and geographic spectrum utilisation efficiency is extremely low. Cognitive radio is an
innovative technology designed to improve spectrum utilisation by exploiting those spectrum
opportunities. This ability is dependent upon spectrum sensing, which is one of most critical
components in a cognitive radio system. A significant challenge is to sense the whole RF
spectrum at a particular physical location in a short observation time. Otherwise, performance
degrades with longer observation times since the lagging response to spectrum holes implies
low spectrum utilisation efficiency. Hence, developing an efficient wideband spectrum sensing
technique is prime important.
In this thesis, a multirate asynchronous sub-Nyquist sampling (MASS) system that employs
multiple low-rate analog-to-digital converters (ADCs) is developed that implements wideband
spectrum sensing. The key features of the MASS system are, 1) low implementation complexity,
2) energy-efficiency for sharing spectrum sensing data, and 3) robustness against the lack
of time synchronisation. The conditions under which recovery of the full spectrum is unique
are presented using compressive sensing (CS) analysis. The MASS system is applied to both
centralised and distributed cognitive radio networks. When the spectra of the cognitive radio
nodes have a common spectral support, using one low-rate ADC in each cognitive radio node
can successfully recover the full spectrum. This is obtained by applying a hybrid matching
pursuit (HMP) algorithm - a synthesis of distributed compressive sensing simultaneous orthogonal
matching pursuit (DCS-SOMP) and compressive sampling matching pursuit (CoSaMP).
Moreover, a multirate spectrum detection (MSD) system is introduced to detect the primary
users from a small number of measurements without ever reconstructing the full spectrum.
To achieve a better detection performance, a data fusion strategy is developed for combining
sensing data from all cognitive radio nodes. Theoretical bounds on detection performance
are derived for distributed cognitive radio nodes suffering from additive white Gaussian noise
(AWGN), Rayleigh fading, and log-normal fading channels.
In conclusion, MASS and MSD both have a low implementation complexity, high energy efficiency,
good data compression capability, and are applicable to distributed cognitive radio
|Sponsor(s): ||Wolfson Microelectronics Scholarship|
Scottish Funding Council for the Joint Research Institute with Heriot-Watt University which is a part of the Edinburgh Research Partnership in Engineering and Mathematics
|Keywords: ||spectrum sensing|
|Appears in Collections:||Engineering thesis and dissertation collection|
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