779 research outputs found
Matrix and Tensor-based ESPRIT Algorithm for Joint Angle and Delay Estimation in 2D Active Broadband Massive MIMO Systems and Analysis of Direction of Arrival Estimation Algorithms for Basal Ice Sheet Tomography
In this thesis, we apply and analyze three direction of arrival algorithms (DoA) to tackle two distinct problems: one belongs to wireless communication, the other to radar signal processing. Though the essence of these two problems is DoA estimation, their formulation, underlying assumptions, application scenario, etc. are totally different. Hence, we write them separately, with ESPRIT algorithm the focus of Part I and MUSIC and MLE detailed in Part II. For wireless communication scenario, mobile data traffic is expected to have an exponential growth in the future. In order to meet the challenge as well as the form factor limitation on the base station, 2D "massive MIMO" has been proposed as one of the enabling technologies to significantly increase the spectral efficiency of a wireless system. In "massive MIMO" systems, a base station will rely on the uplink sounding signals from mobile stations to figure out the spatial information to perform MIMO beamforming. Accordingly, multi-dimensional parameter estimation of a ray-based multi-path wireless channel becomes crucial for such systems to realize the predicted capacity gains. In the first Part, we study joint angle and delay estimation for 2D "massive MIMO" systems in mobile wireless communications. To be specific, we first introduce a low complexity time delay and 2D DoA estimation algorithm based on unitary transformation. Some closed-form results and capacity analysis are involved. Furthermore, the matrix and tensor-based 3D ESPRIT-like algorithms are applied to jointly estimate angles and delay. Significant improvements of the performance can be observed in our communication scheme. Finally, we found that azimuth estimation is more vulnerable compared to elevation estimation. Results suggest that the dimension of the antenna array at the base station plays an important role in determining the estimation performance. These insights will be useful for designing practical "massive MIMO" systems in future mobile wireless communications. For the problem of radar remote sensing of ice sheet topography, one of the key requirements for deriving more realistic ice sheet models is to obtain a good set of basal measurements that enables accurate estimation of bed roughness and conditions. For this purpose, 3D tomography of the ice bed has been successfully implemented with the help of DoA algorithms such as MUSIC and MLE techniques. These methods have enabled fine resolution in the cross-track dimension using synthetic aperture radar (SAR) images obtained from single pass multichannel data. In Part II, we analyze and compare the results obtained from the spectral MUSIC algorithm and an alternating projection (AP) based MLE technique. While the MUSIC algorithm is more attractive computationally compared to MLE, the performance of the latter is known to be superior in most situations. The SAR focused datasets provide a good case study to explore the performance of these two techniques to the application of ice sheet bed elevation estimation. For the antenna array geometry and sample support used in our tomographic application, MUSIC performs better originally using a cross-over analysis where the estimated topography from crossing flightlines are compared for consistency. However, after several improvements applied to MLE, i.e., replacing ideal steering vector generation with measured steering vectors, automatic determination of the number of scatter sources, smoothing the 3D tomography in order to get a more accurate height estimation and introducing a quality metric for the estimated signals, etc., MLE outperforms MUSIC. It confirms that MLE is indeed the optimal estimator for our particular ice bed tomographic application. We observe that, the spatial bottom smoothing, aiming to remove the artifacts made by MLE algorithm, is the most essential step in the post-processing procedure. The 3D tomography we obtained lays a good foundation for further analysis and modeling of ice sheets
Wideband Time-Domain Digital Backpropagation via Subband Processing and Deep Learning
We propose a low-complexity sub-banded DSP architecture for digital
backpropagation where the walk-off effect is compensated using simple delay
elements. For a simulated 96-Gbaud signal and 2500 km optical link, our method
achieves a 2.8 dB SNR improvement over linear equalization.Comment: 3 pages, 3 figur
Spectral unmixing of multiply stained fluorescence samples T
The widespread use of fluorescence microscopy along with the vast library of available fluorescent stains and staining
methods has been extremely beneficial to researchers in many fields, ranging from material sciences to plant biology. In
clinical diagnostics, the ability to combine different markers in a given sample allows the simultaneous detection of the
expression of several different molecules, which in turn provides a powerful diagnostic tool for pathologists, allowing a
better classification of the sample at hand. The correct detection and separation of multiple stains in a sample is achieved
not only by the biochemical and optical properties of the markers, but also by the use of appropriate hardware and software
tools. In this chapter, we will review and compare these tools along with their advantages and limitations
Multiarray Signal Processing: Tensor decomposition meets compressed sensing
We discuss how recently discovered techniques and tools from compressed
sensing can be used in tensor decompositions, with a view towards modeling
signals from multiple arrays of multiple sensors. We show that with appropriate
bounds on a measure of separation between radiating sources called coherence,
one could always guarantee the existence and uniqueness of a best rank-r
approximation of the tensor representing the signal. We also deduce a
computationally feasible variant of Kruskal's uniqueness condition, where the
coherence appears as a proxy for k-rank. Problems of sparsest recovery with an
infinite continuous dictionary, lowest-rank tensor representation, and blind
source separation are treated in a uniform fashion. The decomposition of the
measurement tensor leads to simultaneous localization and extraction of
radiating sources, in an entirely deterministic manner.Comment: 10 pages, 1 figur
Tensor Decompositions for Signal Processing Applications From Two-way to Multiway Component Analysis
The widespread use of multi-sensor technology and the emergence of big
datasets has highlighted the limitations of standard flat-view matrix models
and the necessity to move towards more versatile data analysis tools. We show
that higher-order tensors (i.e., multiway arrays) enable such a fundamental
paradigm shift towards models that are essentially polynomial and whose
uniqueness, unlike the matrix methods, is guaranteed under verymild and natural
conditions. Benefiting fromthe power ofmultilinear algebra as theirmathematical
backbone, data analysis techniques using tensor decompositions are shown to
have great flexibility in the choice of constraints that match data properties,
and to find more general latent components in the data than matrix-based
methods. A comprehensive introduction to tensor decompositions is provided from
a signal processing perspective, starting from the algebraic foundations, via
basic Canonical Polyadic and Tucker models, through to advanced cause-effect
and multi-view data analysis schemes. We show that tensor decompositions enable
natural generalizations of some commonly used signal processing paradigms, such
as canonical correlation and subspace techniques, signal separation, linear
regression, feature extraction and classification. We also cover computational
aspects, and point out how ideas from compressed sensing and scientific
computing may be used for addressing the otherwise unmanageable storage and
manipulation problems associated with big datasets. The concepts are supported
by illustrative real world case studies illuminating the benefits of the tensor
framework, as efficient and promising tools for modern signal processing, data
analysis and machine learning applications; these benefits also extend to
vector/matrix data through tensorization. Keywords: ICA, NMF, CPD, Tucker
decomposition, HOSVD, tensor networks, Tensor Train
Downlink Precoding for Massive MIMO Systems Exploiting Virtual Channel Model Sparsity
In this paper, the problem of designing a forward link linear precoder for
Massive Multiple-Input Multiple-Output (MIMO) systems in conjunction with
Quadrature Amplitude Modulation (QAM) is addressed. First, we employ a novel
and efficient methodology that allows for a sparse representation of multiple
users and groups in a fashion similar to Joint Spatial Division and
Multiplexing. Then, the method is generalized to include Orthogonal Frequency
Division Multiplexing (OFDM) for frequency selective channels, resulting in
Combined Frequency and Spatial Division and Multiplexing, a configuration that
offers high flexibility in Massive MIMO systems. A challenge in such system
design is to consider finite alphabet inputs, especially with larger
constellation sizes such as . The proposed methodology is next
applied jointly with the complexity-reducing Per-Group Processing (PGP)
technique, on a per user group basis, in conjunction with QAM modulation and in
simulations, for constellation size up to . We show by numerical results
that the precoders developed offer significantly better performance than the
configuration with no precoder or the plain beamformer and with
DNN-DANM: A High-Accuracy Two-Dimensional DOA Estimation Method Using Practical RIS
Reconfigurable intelligent surface (RIS) or intelligent reflecting surface
(IRS) has been an attractive technology for future wireless communication and
sensing systems. However, in the practical RIS, the mutual coupling effect
among RIS elements, the reflection phase shift, and amplitude errors will
degrade the RIS performance significantly. This paper investigates the
two-dimensional direction-of-arrival (DOA) estimation problem in the scenario
using a practical RIS. After formulating the system model with the mutual
coupling effect and the reflection phase/amplitude errors of the RIS, a novel
DNNDANM method is proposed for the DOA estimation by combining the deep neural
network (DNN) and the decoupling atomic norm minimization (DANM). The DNN step
reconstructs the received signal from the one with RIS impairments, and the
DANM step exploits the signal sparsity in the two-dimensional spatial domain.
Additionally, a semi-definite programming (SDP) method with low computational
complexity is proposed to solve the atomic minimization problem. Finally, both
simulation and prototype are carried out to show estimation performance, and
the proposed method outperforms the existing methods in the two-dimensional DOA
estimation with low complexity in the scenario with practical RIS.Comment: 11 pages, 12 figure
Multirate Frequency Transformations: Wideband AM-FM Demodulation with Applications to Signal Processing and Communications
The AM-FM (amplitude & frequency modulation) signal model finds numerous applications in image processing, communications, and speech processing. The traditional approaches towards demodulation of signals in this category are the analytic signal approach, frequency tracking, or the energy operator approach. These approaches however, assume that the amplitude and frequency components are slowly time-varying, e.g., narrowband and incur significant demodulation error in the wideband scenarios. In this thesis, we extend a two-stage approach towards wideband AM-FM demodulation that combines multirate frequency transformations (MFT) enacted through a combination of multirate systems with traditional demodulation techniques, e.g., the Teager-Kasiser energy operator demodulation (ESA) approach to large wideband to narrowband conversion factors.
The MFT module comprises of multirate interpolation and heterodyning and converts the wideband AM-FM signal into a narrowband signal, while the demodulation module such as ESA demodulates the narrowband signal into constituent amplitude and frequency components that are then transformed back to yield estimates for the wideband signal.
This MFT-ESA approach is then applied to the various problems of: (a) wideband image demodulation and fingerprint demodulation, where multidimensional energy separation is employed, (b) wideband first-formant demodulation in vowels, and (c) wideband CPM demodulation with partial response signaling, to demonstrate its validity in both monocomponent and multicomponent scenarios as an effective multicomponent AM-FM signal demodulation and analysis technique for image processing, speech processing, and communications based applications
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