Results 1 
4 of
4
Compressive Estimation of Doubly Selective Channels: Exploiting Channel Sparsity to Improve Spectral Efficiency in Multicarrier Transmissions
"... We consider the estimation of doubly selective wireless channels within pulseshaping multicarrier systems (which include OFDM systems as a special case). A pilotassisted channel estimation technique using the methodology of compressed sensing (CS) is proposed. By exploiting a channel’s delayDopple ..."
Abstract

Cited by 36 (1 self)
 Add to MetaCart
(Show Context)
We consider the estimation of doubly selective wireless channels within pulseshaping multicarrier systems (which include OFDM systems as a special case). A pilotassisted channel estimation technique using the methodology of compressed sensing (CS) is proposed. By exploiting a channel’s delayDoppler sparsity, CSbased channel estimation allows an increase in spectral efficiency through a reduction of the number of pilot symbols that have to be transmitted. We also present an extension of our basic channel estimator that employs a sparsityimproving basis expansion. We propose a framework for optimizing the basis and an iterative approximate basis optimization algorithm. Simulation results using three different CS recovery algorithms demonstrate significant performance gains (in terms of improved estimation accuracy or reduction of the number of pilots) relative to conventional leastsquares estimation, as well as substantial advantages of using an optimized basis.
Eigenfunctions of linear systems 1 0.1 EIGENFUNCTIONS OF UNDERSPREAD LINEAR SYSTEMS: THEORY AND APPLICATIONS TO DIGITAL COMMUNI
"... The knowledge of the eigenfunctions of a linear system is a fundamental issue both from the theoretical as well as from the applications point of view. Nonetheless, no analytic solution is available for the eigenfunctions of a general linear system. There are two important classes of contributions s ..."
Abstract
 Add to MetaCart
(Show Context)
The knowledge of the eigenfunctions of a linear system is a fundamental issue both from the theoretical as well as from the applications point of view. Nonetheless, no analytic solution is available for the eigenfunctions of a general linear system. There are two important classes of contributions suggesting analytic expressions for the eigenfunctions of slowlyvarying operators: [5], and the references therein, where it was proved that the eigenfunctions of underspread operators can be approximated by signals whose timefrequency distribution (TFD) is well localized in the timefrequency plane, and [7] where a strict relationship between the instantaneous frequency of the channel eigenfunctions and the contour lines of the Wigner Transform of the operator kernel (or Weyl symbol) was derived for Hermitian slowlyvarying operators. In this article, following an approach similar to [7], we will show that the eigenfunctions can be found exactly for systems whose spread function is concentrated along a straight line and they can be found in approximate sense for those systems whose spread function is maximally concentrated in regions of the Dopplerdelay plane whose area is smaller than one. 0.1.2 Eigenfunctions of timevarying systems The input/output relationship of a continuoustime (CT) linear system is [3]: y(t) = h(t, τ)x(t − τ)dτ (0.1.1) where h(t, τ) is the system impulse response. Although throughout this section we will use the terminology commonly adopted in the transit of signals through timevarying channels, it is worth pointing out that the mathematical formulation is much more general. For example, (0.1.1) can be used to describe the propagation of waves through non homogeneous media and in such a case the independent variables t and τ are spatial coordinates. Following the same notation introduced by Bello [3], any linear timevarying (LTV) channel can be fully characterized by its impulse response h(t, τ), or equivalently by the delayDoppler spread function (or simply spread function) S(ν, τ):= − ∞ h(t, τ)e −j2piνtdt, or by the timevarying transfer function H(t, f):= − ∞ h(t, τ)e
LIST OF TABLES..................................... 7
, 2006
"... I would like to sincerely thank my advisor, Dr. José C. Principe, and coadvisor, Dr. Liuqing Yang, for their support, encouragement and patience in guiding the research. I would also like to thank Dr. John Harris and Dr. Douglas Cenzer, for being on my committee and for their helpful advice. I grat ..."
Abstract
 Add to MetaCart
(Show Context)
I would like to sincerely thank my advisor, Dr. José C. Principe, and coadvisor, Dr. Liuqing Yang, for their support, encouragement and patience in guiding the research. I would also like to thank Dr. John Harris and Dr. Douglas Cenzer, for being on my committee and for their helpful advice. I gratefully acknowledge Dr. Ignacio Santamaría and Javier Vía for providing numerous suggestions and for helpful comments on several of my papers that are part of this dissertation. I also appreciate the helpful discussions from people in the Computational Neuroengineering Lab. Finally, I wish to thank my wife, Hyewon Park, for her support, as well as my dear princesses, Eujin and Jiwon, for providing big motivations to finish my study.