176 research outputs found
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
Wideband Spectrum Sensing on Real-Time Signals at Sub-Nyquist Sampling Rates in Single and Cooperative Multiple Nodes
The authors would like to acknowledge the Engineering and Physical Sciences Research Council (EPSRC) in the UK for their support of this work with Grant No. EP/L024241/1. Mark D. Plumbley was partly supported by a Leadership Fellowship (EP/G007144/1) from the UK EPSR
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
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A Cognitive Radio Compressive Sensing Framework
With the proliferation of wireless devices and services, allied with further significant predicted growth, there is an ever increasing demand for higher transmission rates. This is especially challenging given the limited availability of radio spectrum, and is further exacerbated by a rigid licensing regulatory regime. Spectrum however, is largely underutilized and this has prompted regulators to promote the concept of opportunistic spectrum access. This allows unlicensed secondary users to use bands which are licensed to primary users, but are currently unoccupied, so leading to more efficient spectrum utilization.
A potentially attractive solution to this spectrum underutilisation problem is cognitive radio (CR) technology, which enables the identification and usage of vacant bands by continuously sensing the radio environment, though CR enforces stringent timing requirements and high sampling rates. Compressive sensing (CS) has emerged as a novel sampling paradigm, which provides the theoretical basis to resolve some of these issues, especially for signals exhibiting sparsity in some domain. For CR-related signals however, existing CS architectures such as the random demodulator and compressive multiplexer have limitations in regard to the signal types used, spectrum estimation methods applied, spectral band classification and a dependence on Fourier domain based sparsity.
This thesis presents a new generic CS framework which addresses these issues by specifically embracing three original scientific contributions: i) seamless embedding of the concept of precolouring into existing CS architectures to enhance signal sparsity for CR-related digital modulation schemes; ii) integration of the multitaper spectral estimator to improve sparsity in CR narrowband modulation schemes; and iii) exploiting sparsity in an alternative, non-Fourier (Walsh-Hadamard) domain to expand the applicable CR-related modulation schemes.
Critical analysis reveals the new CS framework provides a consistently superior and robust solution for the recovery of an extensive set of currently employed CR-type signals encountered in wireless communication standards. Significantly, the generic and portable nature of the framework affords the opportunity for further extensions into other CS architectures and sparsity domains
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