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Sensing and Learning Channel State Information in a Dynamic Wireless Environment with Cognitive Radios and Networks

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posted on 01.12.2012, 00:00 authored by Reginald Larenzo Cooper

Sensing and learning channel state information 􁈺CSI􁈻 in a dynamic wireless environment 􁈺DWE􁈻 has not been a focus of the cognitive radio and network research. The focus has been on obtaining spectral resource information about the spectrum availability 􁈺i.e. spectrum sensing for dynamic spectrum access􁈻 or how to adapt radios and networks in a way that improves the effectiveness of the radio networks, for example rate adaptation. In most of these cases it is assume that the CSI is already known or it is obtained from a method that directly measures the spectrum of interest through channel estimation, fading prediction or forecasting, blind estimation, and many others. In DWEs the wireless channel may change at high rates (i.e. fast fading), and methods that directly measure the channel will not provide accurate and timely CSI needed for a cognitive radio or network.

In this thesis a counterintuitive method is presented, indirect channel measurements. Indirect channel measuring is a technique used to determine the transfer function of a desired part of the RF spectrum, which spans several coherence bandwidths, without directly sending a signal through the spectrum of interest, but indirectly through adjacent spectrum. The indirect measuring of the channel has the ability to improve the timeliness and accuracy associated with the obtainment of the CSI in DWEs when using cognitive radios and networks.

This thesis will explain the indirect channel measurements 􁈺ICM􁈻 research and how it can be used in cognitive radio networks for SISO rate adaptation in dynamic wireless environments. Included will be an analytical and empirical analysis of the performance of the ICM technique and other comparable and contrasting techniques. This thesis will also show which technique is best suited for a particular set of parameters for a given dynamic wireless environment. A contextual explanation will also be given to provide a visual picture of a cognitive radio network that will be able to implement the ICM technique using rate adaptation, and its ability to be implemented in hardware.




Degree Type



Electrical and Computer Engineering

Degree Name

Doctor of Philosophy (PhD)


Daniel Stancil

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