7,863 research outputs found
Joint Design and Separation Principle for Opportunistic Spectrum Access in the Presence of Sensing Errors
We address the design of opportunistic spectrum access (OSA) strategies that
allow secondary users to independently search for and exploit instantaneous
spectrum availability. Integrated in the joint design are three basic
components: a spectrum sensor that identifies spectrum opportunities, a sensing
strategy that determines which channels in the spectrum to sense, and an access
strategy that decides whether to access based on imperfect sensing outcomes.
We formulate the joint PHY-MAC design of OSA as a constrained partially
observable Markov decision process (POMDP). Constrained POMDPs generally
require randomized policies to achieve optimality, which are often intractable.
By exploiting the rich structure of the underlying problem, we establish a
separation principle for the joint design of OSA. This separation principle
reveals the optimality of myopic policies for the design of the spectrum sensor
and the access strategy, leading to closed-form optimal solutions. Furthermore,
decoupling the design of the sensing strategy from that of the spectrum sensor
and the access strategy, the separation principle reduces the constrained POMDP
to an unconstrained one, which admits deterministic optimal policies. Numerical
examples are provided to study the design tradeoffs, the interaction between
the spectrum sensor and the sensing and access strategies, and the robustness
of the ensuing design to model mismatch.Comment: 43 pages, 10 figures, submitted to IEEE Transactions on Information
Theory in Feb. 200
LMPIT-inspired Tests for Detecting a Cyclostationary Signal in Noise with Spatio-Temporal Structure
In spectrum sensing for cognitive radio, the presence of a primary user can
be detected by making use of the cyclostationarity property of digital
communication signals. For the general scenario of a cyclostationary signal in
temporally colored and spatially correlated noise, it has previously been shown
that an asymptotic generalized likelihood ratio test (GLRT) and locally most
powerful invariant test (LMPIT) exist. In this paper, we derive detectors for
the presence of a cyclostationary signal in various scenarios with structured
noise. In particular, we consider noise that is temporally white and/or
spatially uncorrelated. Detectors that make use of this additional information
about the noise process have enhanced performance. We have previously derived
GLRTs for these specific scenarios; here, we examine the existence of LMPITs.
We show that these exist only for detecting the presence of a cyclostationary
signal in spatially uncorrelated noise. For white noise, an LMPIT does not
exist. Instead, we propose tests that approximate the LMPIT, and they are shown
to perform well in simulations. Finally, if the noise structure is not known in
advance, we also present hypothesis tests using our framework
Spectrum Sensing in the Presence of Multiple Primary Users
We consider multi-antenna cooperative spectrum sensing in cognitive radio
networks, when there may be multiple primary users. A detector based on the
spherical test is analyzed in such a scenario. Based on the moments of the
distributions involved, simple and accurate analytical formulae for the key
performance metrics of the detector are derived. The false alarm and the
detection probabilities, as well as the detection threshold and Receiver
Operation Characteristics are available in closed form. Simulations are
provided to verify the accuracy of the derived results, and to compare with
other detectors in realistic sensing scenarios.Comment: Accepted in IEEE Transactions on Communication
An asymptotic LMPI test for cyclostationarity detection with application to cognitive radio
We propose a new detector of primary users in cognitive radio networks. The main novelty of the proposed detector in comparison to most known detectors is that it is based on sound statistical principles for detecting cyclostationary signals. In particular, the proposed detector is (asymptotically) the locally most powerful invariant test, i.e. the best invariant detector for low signal-to-noise ratios. The derivation is based on two main ideas: the relationship between a scalar-valued cyclostationary signal and a vector-valued wide-sense stationary signal, and Wijsman's theorem. Moreover, using the spectral representation for the cyclostationary time series, the detector has an insightful interpretation, and implementation, as the broadband coherence between frequencies that are separated by multiples of the cycle frequency. Finally, simulations confirm that the proposed detector performs better than previous approaches.The work of P. Schreier was supported by the Alfried Krupp von Bohlen und Halbach Foundation, under its program “Return of German scientists from abroad”. The work of I. SantamarĂa and J. VĂa was supported by the Spanish Government, Ministerio de Ciencia e InnovaciĂłn (MICINN), under project RACHEL (TEC2013-47141-C4-3-R). The work of L. Scharf was supported by the Airforce Office of Scientific Research under contract FA9550-10-1-0241
Geo-rectification and cloud-cover correction of multi-temporal Earth observation imagery
Over the past decades, improvements in remote sensing technology have led to mass proliferation of aerial imagery. This, in turn, opened vast new possibilities relating to land cover classification, cartography, and so forth.
As applications in these fields became increasingly more complex, the amount of data required also rose accordingly and so, to satisfy these new needs, automated systems had to be developed. Geometric distortions in raw imagery must be rectified, otherwise the high accuracy requirements of the newest applications will not be attained.
This dissertation proposes an automated solution for the pre-stages of multi-spectral satellite imagery classification, focusing on Fast Fourier Shift theorem based geo-rectification and multi-temporal cloud-cover correction.
By automatizing the first stages of image processing, automatic classifiers can take advantage of a larger supply of image data, eventually allowing for the creation of semi-real-time mapping applications
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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