34 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
Recovery under Side Constraints
This paper addresses sparse signal reconstruction under various types of
structural side constraints with applications in multi-antenna systems. Side
constraints may result from prior information on the measurement system and the
sparse signal structure. They may involve the structure of the sensing matrix,
the structure of the non-zero support values, the temporal structure of the
sparse representationvector, and the nonlinear measurement structure. First, we
demonstrate how a priori information in form of structural side constraints
influence recovery guarantees (null space properties) using L1-minimization.
Furthermore, for constant modulus signals, signals with row-, block- and
rank-sparsity, as well as non-circular signals, we illustrate how structural
prior information can be used to devise efficient algorithms with improved
recovery performance and reduced computational complexity. Finally, we address
the measurement system design for linear and nonlinear measurements of sparse
signals. Moreover, we discuss the linear mixing matrix design based on
coherence minimization. Then we extend our focus to nonlinear measurement
systems where we design parallel optimization algorithms to efficiently compute
stationary points in the sparse phase retrieval problem with and without
dictionary learning
Power Allocation and Parameter Estimation for Multipath-based 5G Positioning
We consider a single-anchor multiple-input multiple-output (MIMO) orthogonal
frequency-division multiplexing (OFDM) system with imperfectly synchronized
transmitter (Tx) and receiver (Rx) clocks, where the Rx estimates its position
based on the received reference signals. The Tx, having (imperfect) prior
knowledge about the Rx location and the surrounding geometry, transmits the
reference signals based on a set of fixed beams. In this work, we develop
strategies for the power allocation among the beams aiming to minimize the
expected Cram\'er-Rao lower bound (CRLB) for Rx positioning. Additional
constraints on the design are included to ensure that the line-of-sight (LOS)
path is detected with high probability. Furthermore, the effect of clock
asynchronism on the resulting allocation strategies is also studied. We also
propose a gridless compressed sensing-based position estimation algorithm,
which exploits the information on the clock offset provided by
non-line-of-sight paths, and show that it is asymptotically efficient.Comment: 30 pages, 6 figures, submitted to IEEE Transactions on Wireless
Communication
Low-Rank Channel Estimation for Millimeter Wave and Terahertz Hybrid MIMO Systems
Massive multiple-input multiple-output (MIMO) is one of the fundamental technologies for 5G and beyond. The increased number of antenna elements at both the transmitter and the receiver translates into a large-dimension channel matrix. In addition, the power requirements for the massive MIMO systems are high, especially when fully digital transceivers are deployed. To address this challenge, hybrid analog-digital transceivers are considered a viable alternative. However, for hybrid systems, the number of observations during each channel use is reduced. The high dimensions of the channel matrix and the reduced number of observations make the channel estimation task challenging. Thus, channel estimation may require increased training overhead and higher computational complexity.
The need for high data rates is increasing rapidly, forcing a shift of wireless communication towards higher frequency bands such as millimeter Wave (mmWave) and terahertz (THz). The wireless channel at these bands is comprised of only a few dominant paths. This makes the channel sparse in the angular domain and the resulting channel matrix has a low rank. This thesis aims to provide channel estimation solutions benefiting from the low rankness and sparse nature of the channel. The motivation behind this thesis is to offer a desirable trade-off between training overhead and computational complexity while providing a desirable estimate of the channel