38 research outputs found
RSB Decoupling Property of MAP Estimators
The large-system decoupling property of a MAP estimator is studied when it
estimates the i.i.d. vector from the observation
with
being chosen from a wide range of matrix ensembles, and the noise vector
being i.i.d. and Gaussian. Using the replica method, we show
that the marginal joint distribution of any two corresponding input and output
symbols converges to a deterministic distribution which describes the
input-output distribution of a single user system followed by a MAP estimator.
Under the RSB assumption, the single user system is a scalar channel with
additive noise where the noise term is given by the sum of an independent
Gaussian random variable and correlated interference terms. As the RSB
assumption reduces to RS, the interference terms vanish which results in the
formerly studied RS decoupling principle.Comment: 5 pages, presented in Information Theory Workshop 201
Asymptotics of Nonlinear LSE Precoders with Applications to Transmit Antenna Selection
This paper studies the large-system performance of Least Square Error (LSE)
precoders which~minimize~the~input-output distortion over an arbitrary support
subject to a general penalty function. The asymptotics are determined via the
replica method in a general form which encloses the Replica Symmetric (RS) and
Replica Symmetry Breaking (RSB) ans\"atze. As a result, the "marginal
decoupling property" of LSE precoders for -steps of RSB is derived. The
generality of the studied setup enables us to address special cases in which
the number of active transmit antennas are constrained. Our numerical
investigations depict that the computationally efficient forms of LSE precoders
based on "-norm" minimization perform close to the cases with
"zero-norm" penalty function which have a considerable improvements compared to
the random antenna selection. For the case with BPSK signals and restricted
number of active antennas, the results show that RS fails to predict the
performance while the RSB ansatz is consistent with theoretical bounds.Comment: 5 pages; 2 figures; to be presented at ISIT 201
Replica Symmetry Breaking in Compressive Sensing
For noisy compressive sensing systems, the asymptotic distortion with respect
to an arbitrary distortion function is determined when a general class of
least-square based reconstruction schemes is employed. The sampling matrix is
considered to belong to a large ensemble of random matrices including i.i.d.
and projector matrices, and the source vector is assumed to be i.i.d. with a
desired distribution. We take a statistical mechanical approach by representing
the asymptotic distortion as a macroscopic parameter of a spin glass and
employing the replica method for the large-system analysis. In contrast to
earlier studies, we evaluate the general replica ansatz which includes the RS
ansatz as well as RSB. The generality of the solution enables us to study the
impact of symmetry breaking. Our numerical investigations depict that for the
reconstruction scheme with the "zero-norm" penalty function, the RS fails to
predict the asymptotic distortion for relatively large compression rates;
however, the one-step RSB ansatz gives a valid prediction of the performance
within a larger regime of compression rates.Comment: 7 pages, 3 figures, presented at ITA 201
Asymptotic Analysis of MAP Estimation via the Replica Method and Applications to Compressed Sensing
The replica method is a non-rigorous but well-known technique from
statistical physics used in the asymptotic analysis of large, random, nonlinear
problems. This paper applies the replica method, under the assumption of
replica symmetry, to study estimators that are maximum a posteriori (MAP) under
a postulated prior distribution. It is shown that with random linear
measurements and Gaussian noise, the replica-symmetric prediction of the
asymptotic behavior of the postulated MAP estimate of an n-dimensional vector
"decouples" as n scalar postulated MAP estimators. The result is based on
applying a hardening argument to the replica analysis of postulated posterior
mean estimators of Tanaka and of Guo and Verdu.
The replica-symmetric postulated MAP analysis can be readily applied to many
estimators used in compressed sensing, including basis pursuit, lasso, linear
estimation with thresholding, and zero norm-regularized estimation. In the case
of lasso estimation the scalar estimator reduces to a soft-thresholding
operator, and for zero norm-regularized estimation it reduces to a
hard-threshold. Among other benefits, the replica method provides a
computationally-tractable method for precisely predicting various performance
metrics including mean-squared error and sparsity pattern recovery probability.Comment: 22 pages; added details on the replica symmetry assumptio
Compressed Sensing Performance Analysis via Replica Method using Bayesian framework
Compressive sensing (CS) is a new methodology to capture signals at lower
rate than the Nyquist sampling rate when the signals are sparse or sparse in
some domain. The performance of CS estimators is analyzed in this paper using
tools from statistical mechanics, especially called replica method. This method
has been used to analyze communication systems like Code Division Multiple
Access (CDMA) and multiple input multi- ple output (MIMO) systems with large
size. Replica analysis, now days rigorously proved, is an efficient tool to
analyze large systems in general. Specifically, we analyze the performance of
some of the estimators used in CS like LASSO (the Least Absolute Shrinkage and
Selection Operator) estimator and Zero-Norm regularizing estimator as a special
case of maximum a posteriori (MAP) estimator by using Bayesian framework to
connect the CS estimators and replica method. We use both replica symmetric
(RS) ansatz and one-step replica symmetry breaking (1RSB) ansatz, clamming the
latter is efficient when the problem is not convex. This work is more
analytical in its form. It is deferred for next step to focus on the numerical
results.Comment: The analytical work and results were presented at the 2012 IEEE
European School of Information Theory in Antalya, Turkey between the 16th and
the 20th of Apri