PhD Theses.Spectrum scarcity is a major challenge in wireless communication systems with their
rapid evolutions towards more capacity and bandwidth. The fact that the real-world
spectrum, as a nite resource, is sparsely utilized in certain bands spurs the proposal
of spectrum sharing. In wideband scenarios, accurate real-time spectrum sensing, as an
enabler of spectrum sharing, can become ine cient as it naturally requires the sampling
rate of the analog-to-digital conversion to exceed the Nyquist rate, which is resourcecostly
and energy-consuming. Compressive sensing techniques have been applied in
wideband spectrum sensing to achieve sub-Nyquist-rate sampling of frequency sparse
signals to alleviate such burdens.
A major challenge of compressive spectrum sensing (CSS) is the complexity of the sparse
recovery algorithm. Greedy algorithms achieve sparse recovery with low complexity but
the required prior knowledge of the signal sparsity. A practical spectrum sparsity estimation
scheme is proposed. Furthermore, the dimension of the sparse recovery problem
is proposed to be reduced, which further reduces the complexity and achieves signal
denoising that promotes recovery delity. The robust detection of incumbent radio is
also a fundamental problem of CSS. To address the energy detection problem in CSS,
the spectrum statistics of the recovered signals are investigated and a practical threshold
adaption scheme for energy detection is proposed. Moreover, it is of particular interest to
seek the challenges and opportunities to implement real-world CSS for systems with large
bandwidth. Initial research on the practical issues towards the real-world realization of
wideband CSS system based on the multicoset sampler architecture is presented.
In all, this thesis provides insights into two critical challenges - low-complexity sparse
recovery and robust energy detection - in the general CSS context, while also looks
into some particular issues towards the real-world CSS implementation based on the
i
multicoset sampler
