145 research outputs found
Simultaneous Source Localization and Polarization Estimation via Non-Orthogonal Joint Diagonalization with Vector-Sensors
Joint estimation of direction-of-arrival (DOA) and polarization with electromagnetic vector-sensors (EMVS) is considered in the framework of complex-valued non-orthogonal joint diagonalization (CNJD). Two new CNJD algorithms are presented, which propose to tackle the high dimensional optimization problem in CNJD via a sequence of simple sub-optimization problems, by using LU or LQ decompositions of the target matrices as well as the Jacobi-type scheme. Furthermore, based on the above CNJD algorithms we present a novel strategy to exploit the multi-dimensional structure present in the second-order statistics of EMVS outputs for simultaneous DOA and polarization estimation. Simulations are provided to compare the proposed strategy with existing tensorial or joint diagonalization based methods
Penalty function-based joint diagonalization approach for convolutive blind separation of nonstationary sources
A new approach for convolutive blind source separation (BSS) by explicitly exploiting the second-order nonstationarity of signals and operating in the frequency domain is proposed. The algorithm accommodates a penalty function within the cross-power spectrum-based cost function and thereby converts the separation problem into a joint diagonalization problem with unconstrained optimization. This leads to a new member of the family of joint diagonalization criteria and a modification of the search direction of the gradient-based descent algorithm. Using this approach, not only can the degenerate solution induced by a unmixing matrix and the effect of large errors within the elements of covariance matrices at low-frequency bins be automatically removed, but in addition, a unifying view to joint diagonalization with unitary or nonunitary constraint is provided. Numerical experiments are presented to verify the performance of the new method, which show that a suitable penalty function may lead the algorithm to a faster convergence and a better performance for the separation of convolved speech signals, in particular, in terms of shape preservation and amplitude ambiguity reduction, as compared with the conventional second-order based algorithms for convolutive mixtures that exploit signal nonstationarity
Efficient Parallel-in-Time Solution of Time-Periodic Problems Using a Multi-Harmonic Coarse Grid Correction
This paper presents a highly-parallelizable parallel-in-time algorithm for
efficient solution of nonlinear time-periodic problems. It is based on the
time-periodic extension of the Parareal method, known to accelerate sequential
computations via parallelization on the fine grid. The proposed approach
reduces the complexity of the periodic Parareal solution by introducing a
simplified Newton algorithm, which allows an additional parallelization on the
coarse grid. In particular, at each Newton iteration a multi-harmonic
correction is performed, which converts the block-cyclic periodic system in the
time domain into a block-diagonal system in the frequency domain, thereby
solving for each frequency component in parallel. The convergence analysis of
the method is discussed for a one-dimensional model problem. The introduced
algorithm and several existing solution approaches are compared via their
application to the eddy current problem for both linear and nonlinear models of
a coaxial cable. Performance of the considered methods is also illustrated for
a three-dimensional transformer model
Pseudospectral Shattering, the Sign Function, and Diagonalization in Nearly Matrix Multiplication Time
We exhibit a randomized algorithm which given a square complex
matrix with and , computes with high probability
invertible and diagonal such that and
in
arithmetic operations on a floating point machine with bits of precision. Here is the number of arithmetic
operations required to multiply two complex matrices numerically
stably, with for every , where
is the exponent of matrix multiplication. The algorithm is a variant
of the spectral bisection algorithm in numerical linear algebra (Beavers and
Denman, 1974). This running time is optimal up to polylogarithmic factors, in
the sense that verifying that a given similarity diagonalizes a matrix requires
at least matrix multiplication time. It significantly improves best previously
provable running times of arithmetic operations for
diagonalization of general matrices (Armentano et al., 2018), and (w.r.t.
dependence on ) arithmetic operations for Hermitian matrices
(Parlett, 1998).
The proof rests on two new ingredients. (1) We show that adding a small
complex Gaussian perturbation to any matrix splits its pseudospectrum into
small well-separated components. This implies that the eigenvalues of the
perturbation have a large minimum gap, a property of independent interest in
random matrix theory. (2) We rigorously analyze Roberts' Newton iteration
method for computing the matrix sign function in finite arithmetic, itself an
open problem in numerical analysis since at least 1986. This is achieved by
controlling the evolution the iterates' pseudospectra using a carefully chosen
sequence of shrinking contour integrals in the complex plane.Comment: 78 pages, 3 figures, comments welcome. Slightly edited intro from
previous version + explicit statement of forward error Theorem (Corolary
1.7). Minor corrections mad
Eigenvector-based multidimensional frequency estimation : identifiability, performance, and applications.
Multidimensional frequency estimation is a classic signal processing problem that has versatile applications in sensor array processing and wireless communications. Eigenvalue-based two-dimensional (2-D) and N -dimensional ( N -D) frequency estimation algorithms have been well documented, however, these algorithms suffer from limited identifiability and demanding computations. This dissertation develops a framework on eigenvector-based N -D frequency estimation, which contains several novel algorithms that estimate a structural matrix from eigenvectors and then resolve the N -D frequencies by dividing the elements of the structural matrix. Compared to the existing eigenvalue-based algorithms, these eigenvector-based algorithms can achieve automatic pairing without an extra frequency pairing step, and tins the computational complexity is reduced. The identifiability, performance, and complexity of these algorithms are also systematically studied. Based on this study, the most relaxed identifiability condition for the N -D frequency estimation problem is given and an effective approach using optimized weighting factors to improve the performance of frequency estimation is developed. These results are applied in wireless communication for time-varying multipath channel estimation and prediction, as well as for joint 2-D Direction-of-arrival (DOA) tracking of multiple moving targets
Linear Transmit-Receive Strategies for Multi-user MIMO Wireless Communications
Die Notwendigkeit zur Unterdrueckung von Interferenzen auf der einen Seite
und zur Ausnutzung der durch Mehrfachzugriffsverfahren erzielbaren Gewinne
auf der anderen Seite rueckte die raeumlichen Mehrfachzugriffsverfahren
(Space Division Multiple Access, SDMA) in den Fokus der Forschung. Ein
Vertreter der raeumlichen Mehrfachzugriffsverfahren, die lineare
Vorkodierung, fand aufgrund steigender Anzahl an Nutzern und Antennen in
heutigen und zukuenftigen Mobilkommunikationssystemen besondere Beachtung,
da diese Verfahren das Design von Algorithmen zur Vorcodierung
vereinfachen. Aus diesem Grund leistet diese Dissertation einen Beitrag zur
Entwicklung linearer Sende- und Empfangstechniken fuer MIMO-Technologie mit
mehreren Nutzern. Zunaechst stellen wir ein Framework zur Approximation des
Datendurchsatzes in Broadcast-MIMO-Kanaelen mit mehreren Nutzern vor. In
diesem Framework nehmen wir das lineare Vorkodierverfahren regularisierte
Blockdiagonalisierung (RBD) an. Durch den Vergleich von Dirty Paper Coding
(DPC) und linearen Vorkodieralgorithmen (z.B. Zero Forcing (ZF) und
Blockdiagonalisierung (BD)) ist es uns moeglich, untere und obere Schranken
fuer den Unterschied bezueglich Datenraten und bezueglich Leistung zwischen
beiden anzugeben. Im Weiteren entwickeln wir einen Algorithmus fuer
koordiniertes Beamforming (Coordinated Beamforming, CBF), dessen Loesung
sich in geschlossener Form angeben laesst. Dieser CBF-Algorithmus basiert
auf der SeDJoCo-Transformation und loest bisher vorhandene Probleme im
Bereich CBF. Im Anschluss schlagen wir einen iterativen CBF-Algorithmus
namens FlexCoBF (flexible coordinated beamforming) fuer
MIMO-Broadcast-Kanaele mit mehreren Nutzern vor. Im Vergleich mit bis dato
existierenden iterativen CBF-Algorithmen kann als vielversprechendster
Vorteil die freie Wahl der linearen Sende- und Empfangsstrategie
herausgestellt werden. Das heisst, jede existierende Methode der linearen
Vorkodierung kann als Sendestrategie genutzt werden, waehrend die Strategie
zum Empfangsbeamforming frei aus MRC oder MMSE gewaehlt werden darf. Im
Hinblick auf Szenarien, in denen Mobilfunkzellen in Clustern
zusammengefasst sind, erweitern wir FlexCoBF noch weiter. Hier wurde das
Konzept der koordinierten Mehrpunktverbindung (Coordinated Multipoint
(CoMP) transmission) integriert. Zuletzt stellen wir drei Moeglichkeiten
vor, Kanalzustandsinformationen (Channel State Information, CSI) unter
verschiedenen Kanalumstaenden zu erlangen. Die Qualitaet der
Kanalzustandsinformationen hat einen starken Einfluss auf die Guete des
Uebertragungssystems. Die durch unsere neuen Algorithmen erzielten
Verbesserungen haben wir mittels numerischer Simulationen von Summenraten
und Bitfehlerraten belegt.In order to combat interference and exploit large multiplexing gains of the
multi-antenna systems, a particular interest in spatial division multiple
access (SDMA) techniques has emerged. Linear precoding techniques, as one
of the SDMA strategies, have obtained more attention due to the fact that
an increasing number of users and antennas involved into the existing and
future mobile communication systems requires a simplification of the
precoding design. Therefore, this thesis contributes to the design of
linear transmit and receive strategies for multi-user MIMO broadcast
channels in a single cell and clustered multiple cells. First, we present a
throughput approximation framework for multi-user MIMO broadcast channels
employing regularized block diagonalization (RBD) linear precoding.
Comparing dirty paper coding (DPC) and linear precoding algorithms (e.g.,
zero forcing (ZF) and block diagonalization (BD)), we further quantify
lower and upper bounds of the rate and power offset between them as a
function of the system parameters such as the number of users and antennas.
Next, we develop a novel closed-form coordinated beamforming (CBF)
algorithm (i.e., SeDJoCo based closed-form CBF) to solve the existing open
problem of CBF. Our new algorithm can support a MIMO system with an
arbitrary number of users and transmit antennas. Moreover, the application
of our new algorithm is not only for CBF, but also for blind source
separation (BSS), since the same mathematical model has been used in BSS
application.Then, we further propose a new iterative CBF algorithm (i.e.,
flexible coordinated beamforming (FlexCoBF)) for multi-user MIMO broadcast
channels. Compared to the existing iterative CBF algorithms, the most
promising advantage of our new algorithm is that it provides freedom in the
choice of the linear transmit and receive beamforming strategies, i.e., any
existing linear precoding method can be chosen as the transmit strategy and
the receive beamforming strategy can be flexibly chosen from MRC or MMSE
receivers. Considering clustered multiple cell scenarios, we extend the
FlexCoBF algorithm further and introduce the concept of the coordinated
multipoint (CoMP) transmission. Finally, we present three strategies for
channel state information (CSI) acquisition regarding various channel
conditions and channel estimation strategies. The CSI knowledge is required
at the base station in order to implement SDMA techniques. The quality of
the obtained CSI heavily affects the system performance. The performance
enhancement achieved by our new strategies has been demonstrated by
numerical simulation results in terms of the system sum rate and the bit
error rate
Advanced optimization algorithms for sensor arrays and multi-antenna communications
Optimization problems arise frequently in sensor array and multi-channel signal processing applications. Often, optimization needs to be performed subject to a matrix constraint. In particular, unitary matrices play a crucial role in communications and sensor array signal processing. They are involved in almost all modern multi-antenna transceiver techniques, as well as sensor array applications in biomedicine, machine learning and vision, astronomy and radars.
In this thesis, algorithms for optimization under unitary matrix constraint stemming from Riemannian geometry are developed. Steepest descent (SD) and conjugate gradient (CG) algorithms operating on the Lie group of unitary matrices are derived. They have the ability to find the optimal solution in a numerically efficient manner and satisfy the constraint accurately. Novel line search methods specially tailored for this type of optimization are also introduced. The proposed approaches exploit the geometrical properties of the constraint space in order to reduce the computational complexity.
Array and multi-channel signal processing techniques are key technologies in wireless communication systems. High capacity and link reliability may be achieved by using multiple transmit and receive antennas. Combining multi-antenna techniques with multicarrier transmission leads to high the spectral efficiency and helps to cope with severe multipath propagation.
The problem of channel equalization in MIMO-OFDM systems is also addressed in this thesis. A blind algorithm that optimizes of a combined criterion in order to be cancel both inter-symbol and co-channel interference is proposed. The algorithm local converge properties are established as well
Variational determination of the two-particle density matrix : the case of doubly-occupied space
The world at the level of the atom is described by the branch of science called quantum mechanics. The crown jewel of quantum mechanics is given by the Schrödinger equation which describes a system of indistinguishable particles, that interact with each other. However, an equation alone is not enough: the solution is what interests us. Unfortunately, the exponential scaling of the Hilbert space makes it unfeasible to calculate the exact wave function.
This dissertation concerns itself with one of the many ab initio methods that were developed to solve this problem: the variational determination of the second-order density matrix. This method already has a long history.
It is not considered to be on par with best ab initio methods.
This work tries an alternative approach. We assume that the wave function has a Slater determinant expansion where all orbitals are doubly occupied or empty. This assumption drastically reduces the scaling of the N-representability conditions. The downside is that the energy explicitly depends on the used orbitals and thus an orbital optimizer is needed. The hope is that by using this approximation, we can capture the lion's share of the static correlation and that any missing dynamic correlation can be added through perturbation theory.
We developed an algorithm based on Jacobi rotations. The scaling is much more favorable compared to the general case. The method is then tested on a array of benchmark systems
General quantum algorithms for Hamiltonian simulation with applications to a non-Abelian lattice gauge theory
With a focus on universal quantum computing for quantum simulation, and
through the example of lattice gauge theories, we introduce rather general
quantum algorithms that can efficiently simulate certain classes of
interactions consisting of correlated changes in multiple (bosonic and
fermionic) quantum numbers with non-trivial functional coefficients. In
particular, we analyze diagonalization of Hamiltonian terms using a
singular-value decomposition technique, and discuss how the achieved diagonal
unitaries in the digitized time-evolution operator can be implemented. The
lattice gauge theory studied is the SU(2) gauge theory in 1+1 dimensions
coupled to one flavor of staggered fermions, for which a complete
quantum-resource analysis within different computational models is presented.
The algorithms are shown to be applicable to higher-dimensional theories as
well as to other Abelian and non-Abelian gauge theories. The example chosen
further demonstrates the importance of adopting efficient theoretical
formulations: it is shown that an explicitly gauge-invariant formulation using
loop, string, and hadron (LSH) degrees of freedom simplifies the algorithms and
lowers the cost compared with the standard formulations based on
angular-momentum as well as the Schwinger-boson degrees of freedom. The LSH
formulation further retains the non-Abelian gauge symmetry despite the
inexactness of the digitized simulation, without the need for costly controlled
operations. Such theoretical and algorithmic considerations are likely to be
essential in quantum simulating other complex theories of relevance to nature.Comment: 59+17+7 pages, 16 figure
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