3,073 research outputs found
S-AMP for Non-linear Observation Models
Recently we extended Approximate message passing (AMP) algorithm to be able
to handle general invariant matrix ensembles. In this contribution we extend
our S-AMP approach to non-linear observation models. We obtain generalized AMP
(GAMP) algorithm as the special case when the measurement matrix has zero-mean
iid Gaussian entries. Our derivation is based upon 1) deriving expectation
propagation (EP) like algorithms from the stationary-points equations of the
Gibbs free energy under first- and second-moment constraints and 2) applying
additive free convolution in free probability theory to get low-complexity
updates for the second moment quantities.Comment: 6 page
S-AMP: Approximate Message Passing for General Matrix Ensembles
In this work we propose a novel iterative estimation algorithm for linear
observation systems called S-AMP whose fixed points are the stationary points
of the exact Gibbs free energy under a set of (first- and second-) moment
consistency constraints in the large system limit. S-AMP extends the
approximate message-passing (AMP) algorithm to general matrix ensembles. The
generalization is based on the S-transform (in free probability) of the
spectrum of the measurement matrix. Furthermore, we show that the optimality of
S-AMP follows directly from its design rather than from solving a separate
optimization problem as done for AMP.Comment: 5 pages, 1 figur
Capacity Scaling in MIMO Systems with General Unitarily Invariant Random Matrices
We investigate the capacity scaling of MIMO systems with the system
dimensions. To that end, we quantify how the mutual information varies when the
number of antennas (at either the receiver or transmitter side) is altered. For
a system comprising receive and transmit antennas with , we find
the following: By removing as many receive antennas as needed to obtain a
square system (provided the channel matrices before and after the removal have
full rank) the maximum resulting loss of mutual information over all
signal-to-noise ratios (SNRs) depends only on , and the matrix of
left-singular vectors of the initial channel matrix, but not on its singular
values. In particular, if the latter matrix is Haar distributed the ergodic
rate loss is given by nats. Under
the same assumption, if with the ratio
fixed, the rate loss normalized by converges almost surely to
bits with denoting the binary entropy function. We also quantify and
study how the mutual information as a function of the system dimensions
deviates from the traditionally assumed linear growth in the minimum of the
system dimensions at high SNR.Comment: Accepted for publication in the IEEE Transactions on Information
Theor
Dynamical Functional Theory for Compressed Sensing
We introduce a theoretical approach for designing generalizations of the
approximate message passing (AMP) algorithm for compressed sensing which are
valid for large observation matrices that are drawn from an invariant random
matrix ensemble. By design, the fixed points of the algorithm obey the
Thouless-Anderson-Palmer (TAP) equations corresponding to the ensemble. Using a
dynamical functional approach we are able to derive an effective stochastic
process for the marginal statistics of a single component of the dynamics. This
allows us to design memory terms in the algorithm in such a way that the
resulting fields become Gaussian random variables allowing for an explicit
analysis. The asymptotic statistics of these fields are consistent with the
replica ansatz of the compressed sensing problem.Comment: 5 pages, accepted for ISIT 201
Tunneling through magnetic molecules with arbitrary angle between easy axis and magnetic field
Inelastic tunneling through magnetically anisotropic molecules is studied
theoretically in the presence of a strong magnetic field. Since the molecular
orientation is not well controlled in tunneling experiments, we consider
arbitrary angles between easy axis and field. This destroys all conservation
laws except that of charge, leading to a rich fine structure in the
differential conductance. Besides single molecules we also study monolayers of
molecules with either aligned or random easy axes. We show that detailed
information on the molecular transitions and orientations can be obtained from
the differential conductance for varying magnetic field. For random easy axes,
averaging over orientations leads to van Hove singularities in the differential
conductance. Rate equations in the sequential-tunneling approximation are
employed. An efficient approximation for their solution for complex molecules
is presented. The results are applied to Mn12-based magnetic molecules.Comment: 10 pages, 10 figures include
An Iterative Receiver for OFDM With Sparsity-Based Parametric Channel Estimation
In this work we design a receiver that iteratively passes soft information
between the channel estimation and data decoding stages. The receiver
incorporates sparsity-based parametric channel estimation. State-of-the-art
sparsity-based iterative receivers simplify the channel estimation problem by
restricting the multipath delays to a grid. Our receiver does not impose such a
restriction. As a result it does not suffer from the leakage effect, which
destroys sparsity. Communication at near capacity rates in high SNR requires a
large modulation order. Due to the close proximity of modulation symbols in
such systems, the grid-based approximation is of insufficient accuracy. We show
numerically that a state-of-the-art iterative receiver with grid-based sparse
channel estimation exhibits a bit-error-rate floor in the high SNR regime. On
the contrary, our receiver performs very close to the perfect channel state
information bound for all SNR values. We also demonstrate both theoretically
and numerically that parametric channel estimation works well in dense
channels, i.e., when the number of multipath components is large and each
individual component cannot be resolved.Comment: Major revision, accepted for IEEE Transactions on Signal Processin
Receiver Architectures for MIMO-OFDM Based on a Combined VMP-SP Algorithm
Iterative information processing, either based on heuristics or analytical
frameworks, has been shown to be a very powerful tool for the design of
efficient, yet feasible, wireless receiver architectures. Within this context,
algorithms performing message-passing on a probabilistic graph, such as the
sum-product (SP) and variational message passing (VMP) algorithms, have become
increasingly popular.
In this contribution, we apply a combined VMP-SP message-passing technique to
the design of receivers for MIMO-ODFM systems. The message-passing equations of
the combined scheme can be obtained from the equations of the stationary points
of a constrained region-based free energy approximation. When applied to a
MIMO-OFDM probabilistic model, we obtain a generic receiver architecture
performing iterative channel weight and noise precision estimation,
equalization and data decoding. We show that this generic scheme can be
particularized to a variety of different receiver structures, ranging from
high-performance iterative structures to low complexity receivers. This allows
for a flexible design of the signal processing specially tailored for the
requirements of each specific application. The numerical assessment of our
solutions, based on Monte Carlo simulations, corroborates the high performance
of the proposed algorithms and their superiority to heuristic approaches
Application of the Evidence Procedure to the Estimation of Wireless Channels
We address the application of the Bayesian evidence procedure to the estimation of wireless channels. The proposed scheme is based on relevance vector machines (RVM) originally proposed by M. Tipping. RVMs allow to estimate channel parameters as well as to assess the number of multipath components constituting the channel within the Bayesian framework by locally maximizing the evidence integral. We show that, in the case of channel sounding using pulse-compression techniques, it is possible to cast the channel model as a general linear model, thus allowing RVM methods to be applied. We extend the original RVM algorithm to the multiple-observation/multiple-sensor scenario by proposing a new graphical model to represent multipath components. Through the analysis of the evidence procedure we develop a thresholding algorithm that is used in estimating the number of components. We also discuss the relationship of the evidence procedure to the standard minimum description length (MDL) criterion. We show that the maximum of the evidence corresponds to the minimum of the MDL criterion. The applicability of the proposed scheme is demonstrated with synthetic as well as real-world channel measurements, and a performance increase over the conventional MDL criterion applied to maximum-likelihood estimates of the channel parameters is observed
Modeling of Reverberant Radio Channels Using Propagation Graphs
In measurements of in-room radio channel responses an avalanche effect can be
observed: earliest signal components, which appear well separated in delay, are
followed by an avalanche of components arriving with increasing rate of
occurrence, gradually merging into a diffuse tail with exponentially decaying
power. We model the channel as a propagation graph in which vertices represent
transmitters, receivers, and scatterers, while edges represent propagation
conditions between vertices. The recursive structure of the graph accounts for
the exponential power decay and the avalanche effect. We derive a closed form
expression for the graph's transfer matrix. This expression is valid for any
number of interactions and is straightforward to use in numerical simulations.
We discuss an example where time dispersion occurs only due to propagation in
between vertices. Numerical experiments reveal that the graph's recursive
structure yields both an exponential power decay and an avalanche effect.Comment: 11 pages, Submitted to IEEE Transactions on Antennas and Propagatio
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