1,346 research outputs found
Energy Efficient Estimation of Gaussian Sources Over Inhomogeneous Gaussian MAC Channels
It has been shown lately the optimality of uncoded transmission in estimating
Gaussian sources over homogeneous/symmetric Gaussian multiple access channels
(MAC) using multiple sensors. It remains, however, unclear whether it still
holds for any arbitrary networks and/or with high channel signal-to-noise ratio
(SNR) and high signal-to-measurement-noise ratio (SMNR). In this paper, we
first provide a joint source and channel coding approach in estimating Gaussian
sources over Gaussian MAC channels, as well as its sufficient and necessary
condition in restoring Gaussian sources with a prescribed distortion value. An
interesting relationship between our proposed joint approach with a more
straightforward separate source and channel coding scheme is then established.
We then formulate constrained power minimization problems and transform them to
relaxed convex geometric programming problems, whose numerical results exhibit
that either separate or uncoded scheme becomes dominant over a linear topology
network. In addition, we prove that the optimal decoding order to minimize the
total transmission powers for both source and channel coding parts is solely
subject to the ranking of MAC channel qualities, and has nothing to do with the
ranking of measurement qualities. Finally, asymptotic results for homogeneous
networks are obtained which not only confirm the existing optimality of the
uncoded approach, but also show that the asymptotic SNR exponents of these
three approaches are all the same. Moreover, the proposed joint approach share
the same asymptotic ratio with respect to high SNR and high SMNR as the uncoded
scheme
Estimation Diversity and Energy Efficiency in Distributed Sensing
Distributed estimation based on measurements from multiple wireless sensors
is investigated. It is assumed that a group of sensors observe the same
quantity in independent additive observation noises with possibly different
variances. The observations are transmitted using amplify-and-forward (analog)
transmissions over non-ideal fading wireless channels from the sensors to a
fusion center, where they are combined to generate an estimate of the observed
quantity. Assuming that the Best Linear Unbiased Estimator (BLUE) is used by
the fusion center, the equal-power transmission strategy is first discussed,
where the system performance is analyzed by introducing the concept of
estimation outage and estimation diversity, and it is shown that there is an
achievable diversity gain on the order of the number of sensors. The optimal
power allocation strategies are then considered for two cases: minimum
distortion under power constraints; and minimum power under distortion
constraints. In the first case, it is shown that by turning off bad sensors,
i.e., sensors with bad channels and bad observation quality, adaptive power
gain can be achieved without sacrificing diversity gain. Here, the adaptive
power gain is similar to the array gain achieved in Multiple-Input
Single-Output (MISO) multi-antenna systems when channel conditions are known to
the transmitter. In the second case, the sum power is minimized under
zero-outage estimation distortion constraint, and some related energy
efficiency issues in sensor networks are discussed.Comment: To appear at IEEE Transactions on Signal Processin
Distributed detection, localization, and estimation in time-critical wireless sensor networks
In this thesis the problem of distributed detection, localization, and estimation
(DDLE) of a stationary target in a fusion center (FC) based wireless sensor network
(WSN) is considered. The communication process is subject to time-critical
operation, restricted power and bandwidth (BW) resources operating over a shared
communication channel Buffering from Rayleigh fading and phase noise. A novel algorithm
is proposed to solve the DDLE problem consisting of two dependent stages:
distributed detection and distributed estimation. The WSN performs distributed
detection first and based on the global detection decision the distributed estimation
stage is performed. The communication between the SNs and the FC occurs over a
shared channel via a slotted Aloha MAC protocol to conserve BW.
In distributed detection, hard decision fusion is adopted, using the counting
rule (CR), and sensor censoring in order to save power and BW. The effect of
Rayleigh fading on distributed detection is also considered and accounted for by
using distributed diversity combining techniques where the diversity combining is
among the sensor nodes (SNs) in lieu of having the processing done at the FC.
Two distributed techniques are proposed: the distributed maximum ratio combining
(dMRC) and the distributed Equal Gain Combining (dEGC). Both techniques show
superior detection performance when compared to conventional diversity combining
procedures that take place at the FC.
In distributed estimation, the segmented distributed localization and estimation
(SDLE) framework is proposed. The SDLE enables efficient power and BW
processing. The SOLE hinges on the idea of introducing intermediate parameters
that are estimated locally by the SNs and transmitted to the FC instead of the
actual measurements. This concept decouples the main problem into a simpler set
of local estimation problems solved at the SNs and a global estimation problem
solved at the FC. Two algorithms are proposed for solving the local problem: a
nonlinear least squares (NLS) algorithm using the variable projection (VP) method
and a simpler gird search (GS) method. Also, Four algorithms are proposed to solve
the global problem: NLS, GS, hyperspherical intersection method (HSI), and robust
hyperspherical intersection (RHSI) method. Thus, the SDLE can be solved through
local and global algorithm combinations. Five combinations are tied: NLS2 (NLS-NLS),
NLS-HSI, NLS-RHSI, GS2, and GS-N LS. It turns out that the last algorithm
combination delivers the best localization and estimation performance. In fact , the
target can be localized with less than one meter error.
The SNs send their local estimates to the FC over a shared channel using the
slotted-Aloha MAC protocol, which suits WSNs since it requires only one channel.
However, Aloha is known for its relatively high medium access or contention delay
given the medium access probability is poorly chosen. This fact significantly
hinders the time-critical operation of the system. Hence, multi-packet reception
(MPR) is used with slotted Aloha protocol, in which several channels are used for
contention. The contention delay is analyzed for slotted Aloha with and without
MPR. More specifically, the mean and variance have been analytically computed
and the contention delay distribution is approximated. Having theoretical expressions
for the contention delay statistics enables optimizing both the medium access
probability and the number of MPR channels in order to strike a trade-off between
delay performance and complexity
Data analysis methods for the cosmic microwave background
41 pages, 21 figuresInternational audienceIn this review, we give an overview of some of the major aspects of data reduction and analysis for the cosmic microwave background (CMB). Since its prediction and discovery in the last century, the CMB radiation has proven itself to be one of our most valuable tools for precision cosmology. Recently, and especially when combined with complementary cosmological data, measurements of the CMB anisotropies have provided us with a wealth of quantitive information about the birth, evolution and structure of our Universe. We begin with a simple, general introduction to the physics of the CMB, including a basic overview of the experiments which record CMB data. The focus, however, will be the data analysis treatment of CMB data sets
A unified pseudo- framework
The pseudo- is an algorithm for estimating the angular power and
cross-power spectra that is very fast and, in realistic cases, also nearly
optimal. The algorithm can be extended to deal with contaminant deprojection
and purification, and can therefore be applied in a wide variety of
scenarios of interest for current and future cosmological observations. This
paper presents NaMaster, a public, validated, accurate and easy-to-use software
package that, for the first time, provides a unified framework to compute
angular cross-power spectra of any pair of spin-0 or spin-2 fields,
contaminated by an arbitrary number of linear systematics and requiring - or
-mode purification, both on the sphere or in the flat-sky approximation. We
describe the mathematical background of the estimator, including all the
features above, and its software implementation in NaMaster. We construct a
validation suite that aims to resemble the types of observations that
next-generation large-scale structure and ground-based CMB experiments will
face, and use it to show that the code is able to recover the input power
spectra in the most complex scenarios with no detectable bias. NaMaster can be
found at https://github.com/LSSTDESC/NaMaster, and is provided with
comprehensive documentation and a number of code examples.Comment: 27 pages, 17 figures, accepted in MNRAS. Code can be found at
https://github.com/LSSTDESC/NaMaste
Stochastic Modeling and Estimation of Wireless Channels with Application to Ultra Wide Band Systems
This thesis is concerned with modeling of both space and time variations of Ultra Wide Band (UWB) indoor channels. The most common empirically determined amplitude distribution in many UWB environments is Nakagami distribution. The latter is generalized to stochastic diffusion processes which capture the dynamics of UWB channels. In contrast with the traditional models, the statistics of the proposed models are shown to be time varying, but converge in steady state to their static counterparts.
System identification algorithms are used to extract various channel parameters using received signal measurement data, which are usually available at the receiver. The expectation maximization (EM) algorithm and the Kalman filter (KF) are employed in estimating channel parameters as well as the inphase and quadrature components, respectively. The proposed algorithms are recursive and therefore can be implemented in real time. Further, sufficient conditions for the convergence of the EM algorithm are provided. Comparison with recursive Least-square (LS) algorithms is carried out using experimental measurements.
Distributed stochastic power control algorithms based on the fixed point theorem and stochastic approximations are used to solve for the optimal transmit power problem and numerical results are also presented.
A framework which can capture the statistics of the overall received signal and a methodology to estimate parameters of the counting process based on the received signal is developed. Furthermore, second moment statistics and characteristic functions are computed explicitly and considered as an extension of Rice’s shot noise analysis.
Another two important components, input design and model selection are also considered. Gel’fand n-widths and Time n-widths are used to represent the inherent error introduced by input design. Kolmogorov n-width is used to characterize the representation error introduced by model selection. In particular, it is shown that the optimal model for reducing the representation error is a finite impulse response (FIR) model and the optimal input is an impulse at the start of the observation interval
- …