2,547 research outputs found
5G Positioning and Mapping with Diffuse Multipath
5G mmWave communication is useful for positioning due to the geometric
connection between the propagation channel and the propagation environment.
Channel estimation methods can exploit the resulting sparsity to estimate
parameters(delay and angles) of each propagation path, which in turn can be
exploited for positioning and mapping. When paths exhibit significant spread in
either angle or delay, these methods breakdown or lead to significant biases.
We present a novel tensor-based method for channel estimation that allows
estimation of mmWave channel parameters in a non-parametric form. The method is
able to accurately estimate the channel, even in the absence of a specular
component. This in turn enables positioning and mapping using only diffuse
multipath. Simulation results are provided to demonstrate the efficacy of the
proposed approach
On the Coherence Properties of Random Euclidean Distance Matrices
In the present paper we focus on the coherence properties of general random
Euclidean distance matrices, which are very closely related to the respective
matrix completion problem. This problem is of great interest in several
applications such as node localization in sensor networks with limited
connectivity. Our results can directly provide the sufficient conditions under
which an EDM can be successfully recovered with high probability from a limited
number of measurements.Comment: 5 pages, SPAWC 201
Joint Localization and Orientation Estimation in Millimeter-Wave MIMO OFDM Systems via Atomic Norm Minimization
Herein, an atomic norm based method for accurately estimating the location
and orientation of a target from millimeter-wave multi-input-multi-output
(MIMO) orthogonal frequency-division multiplexing (OFDM) signals is presented.
A novel virtual channel matrix is introduced and an algorithm to extract
localization-relevant channel parameters from its atomic norm decomposition is
designed. Then, based on the extended invariance principle, a weighted least
squares problem is proposed to accurately recover the location and orientation
using both line-of-sight and non-line-of-sight channel information. The
conditions for the optimality and uniqueness of the estimate and theoretical
guarantees for the estimation error are characterized for the noiseless and the
noisy scenarios. Theoretical results are confirmed via simulation. Numerical
results investigate the robustness of the proposed algorithm to incorrect model
order selection or synchronization error, and highlight performance
improvements over a prior method. The resultant performance nearly achieves the
Cramer-Rao lower bound on the estimation error.Comment: arXiv admin note: text overlap with arXiv:2110.0440
Wavelets and their use
This review paper is intended to give a useful guide for those who want to
apply discrete wavelets in their practice. The notion of wavelets and their use
in practical computing and various applications are briefly described, but
rigorous proofs of mathematical statements are omitted, and the reader is just
referred to corresponding literature. The multiresolution analysis and fast
wavelet transform became a standard procedure for dealing with discrete
wavelets. The proper choice of a wavelet and use of nonstandard matrix
multiplication are often crucial for achievement of a goal. Analysis of various
functions with the help of wavelets allows to reveal fractal structures,
singularities etc. Wavelet transform of operator expressions helps solve some
equations. In practical applications one deals often with the discretized
functions, and the problem of stability of wavelet transform and corresponding
numerical algorithms becomes important. After discussing all these topics we
turn to practical applications of the wavelet machinery. They are so numerous
that we have to limit ourselves by some examples only. The authors would be
grateful for any comments which improve this review paper and move us closer to
the goal proclaimed in the first phrase of the abstract.Comment: 63 pages with 22 ps-figures, to be published in Physics-Uspekh
High-Resolution Indoor Sensing Using Channel State Information of WiFi Networks
Indoor sensing is becoming increasingly important over time as it can be effectively utilized in many applications from digital health care systems to indoor safety and security systems. In particular, implementing sensing operations using existing infrastructures improves our experience and well-being, and exhibits unique advantages. The physical layer channel state information for wireless fidelity (WiFi) communications carries rich information about scatters in the propagation environment; hence, we exploited this information to enable detailed recognition of human behaviours in this study. Comprehensive calibration and filtering techniques were developed to alleviate the redundant responses embedded in the channel state information (CSI) data due to static objects and accidental events. Accurate information on breathing rate, heartbeat and angle of arrival of the incoming signal at the receiver side was inferred from the available CSI data. The method and procedure developed can be extended for sensing or imaging the environment utilizing wireless communication networks
Wavelets: mathematics and applications
The notion of wavelets is defined. It is briefly described {\it what} are
wavelets, {\it how} to use them, {\it when} we do need them, {\it why} they are
preferred and {\it where} they have been applied. Then one proceeds to the
multiresolution analysis and fast wavelet transform as a standard procedure for
dealing with discrete wavelets. It is shown which specific features of signals
(functions) can be revealed by this analysis, but can not be found by other
methods (e.g., by the Fourier expansion). Finally, some examples of practical
application are given (in particular, to analysis of multiparticle production}.
Rigorous proofs of mathematical statements are omitted, and the reader is
referred to the corresponding literature.Comment: 16 pages, 5 figures, Latex, Phys. Atom. Nuc
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