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Sub-Nyquist Wideband Spectrum Sensing and Sharing
PhDThe rising popularity of wireless services resulting in spectrum shortage has motivated
dynamic spectrum sharing to facilitate e cient usage of the underutilized spectrum.
Wideband spectrum sensing is a critical functionality to enable dynamic spectrum access
by enhancing the opportunities of exploring spectral holes, but entails a major implemen-
tation challenge in compact commodity radios that have limited energy and computation
capabilities. The sampling rates speci ed by the Shannon-Nyquist theorem impose great
challenges both on the acquisition hardware and the subsequent storage and digital sig-
nal processors. Sub-Nyquist sampling was thus motivated to sample wideband signals
at rates far lower than the Nyquist rate, while still retaining the essential information in
the underlying signals.
This thesis proposes several algorithms for invoking sub-Nyquist sampling in wideband
spectrum sensing. Speci cally, a sub-Nyquist wideband spectrum sensing algorithm is
proposed that achieves wideband sensing independent of signal sparsity without sampling
at full bandwidth by using the low-speed analog-to-digital converters based on sparse
Fast Fourier Transform. To lower signal spectrum sparsity while maintaining the channel
state information, the received signal is pre-processed through a proposed permutation
and ltering algorithm. Additionally, a low-complexity sub-Nyquist wideband spectrum
sensing scheme is proposed that locates occupied channels blindly by recovering the sig-
nal support, based on the jointly sparse nature of multiband signals. Exploiting the
common signal support shared among multiple secondary users, an e cient coopera-
tive spectrum sensing scheme is developed, in which the energy consumption on signal
acquisition, processing, and transmission is reduced with the detection performance guar-
antee. To further reduce the computation complexity of wideband spectrum sensing, a
hybrid framework of sub-Nyquist wideband spectrum sensing with geolocation database
is explored. Prior channel information from geolocation database is utilized in the sens-
ing process to reduce the processing requirements on the sensor nodes. The models of
the proposed algorithms are derived and veri ed by numerical analyses and tested on
both real-world and simulated TV white space signals
Sub-Nyquist Sampling: Bridging Theory and Practice
Sampling theory encompasses all aspects related to the conversion of
continuous-time signals to discrete streams of numbers. The famous
Shannon-Nyquist theorem has become a landmark in the development of digital
signal processing. In modern applications, an increasingly number of functions
is being pushed forward to sophisticated software algorithms, leaving only
those delicate finely-tuned tasks for the circuit level.
In this paper, we review sampling strategies which target reduction of the
ADC rate below Nyquist. Our survey covers classic works from the early 50's of
the previous century through recent publications from the past several years.
The prime focus is bridging theory and practice, that is to pinpoint the
potential of sub-Nyquist strategies to emerge from the math to the hardware. In
that spirit, we integrate contemporary theoretical viewpoints, which study
signal modeling in a union of subspaces, together with a taste of practical
aspects, namely how the avant-garde modalities boil down to concrete signal
processing systems. Our hope is that this presentation style will attract the
interest of both researchers and engineers in the hope of promoting the
sub-Nyquist premise into practical applications, and encouraging further
research into this exciting new frontier.Comment: 48 pages, 18 figures, to appear in IEEE Signal Processing Magazin
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