1,223 research outputs found
Atomic Norm decomposition for sparse model reconstruction applied to positioning and wireless communications
This thesis explores the recovery of sparse signals, arising in the wireless communication and radar system fields, via atomic norm decomposition. Particularly, we
focus on compressed sensing gridless methodologies, which avoid the always existing
error due to the discretization of a continuous space in on-grid methods. We define
the sparse signal by means of a linear combination of so called atoms defined in a
continuous parametrical atom set with infinite cardinality. Those atoms are fully
characterized by a multi-dimensional parameter containing very relevant information
about the application scenario itself. Also, the number of composite atoms is
much lower than the dimension of the problem, which yields sparsity. We address
a gridless optimization solution enforcing sparsity via atomic norm minimization to
extract the parameters that characterize the atom from an observed measurement
of the model, which enables model recovery. We also study a machine learning approach to estimate the number of composite atoms that construct the model, given
that in certain scenarios this number is unknown.
The applications studied in the thesis lay on the field of wireless communications,
particularly on MIMO mmWave channels, which due to their natural properties can
be modeled as sparse. We apply the proposed methods to positioning in automotive
pulse radar working in the mmWave range, where we extract relevant information
such as angle of arrival (AoA), distance and velocity from the received echoes of
objects or targets. Next we study the design of a hybrid precoder for mmWave
channels which allows the reduction of hardware cost in the system by minimizing
as much as possible the number of required RF chains. Last, we explore full channel
estimation by finding the angular parameters that model the channel. For all
the applications we provide a numerical analysis where we compare our proposed
method with state-of-the-art techniques, showing that our proposal outperforms the
alternative methods.Programa de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidente: Juan José Murillo Fuentes.- Secretario: Pablo Martínez Olmos.- Vocal: David Luengo Garcí
Fast Fiber Orientation Estimation in Diffusion MRI from kq-Space Sampling and Anatomical Priors
High spatio-angular resolution diffusion MRI (dMRI) has been shown to provide
accurate identification of complex fiber configurations, albeit at the cost of
long acquisition times. We propose a method to recover intra-voxel fiber
configurations at high spatio-angular resolution relying on a kq-space
under-sampling scheme to enable accelerated acquisitions. The inverse problem
for reconstruction of the fiber orientation distribution (FOD) is regularized
by a structured sparsity prior promoting simultaneously voxelwise sparsity and
spatial smoothness of fiber orientation. Prior knowledge of the spatial
distribution of white matter, gray matter and cerebrospinal fluid is also
assumed. A minimization problem is formulated and solved via a forward-backward
convex optimization algorithmic structure. Simulations and real data analysis
suggest that accurate FOD mapping can be achieved from severe kq-space
under-sampling regimes, potentially enabling high spatio-angular dMRI in the
clinical setting.Comment: 10 pages, 5 figures, Supplementary Material
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