17,802 research outputs found
Fast performance estimation of block codes
Importance sampling is used in this paper to address the classical yet important problem of performance estimation of block codes. Simulation distributions that comprise discreteand continuous-mixture probability densities are motivated and used for this application. These mixtures are employed in concert with the so-called g-method, which is a conditional importance sampling technique that more effectively exploits knowledge of underlying input distributions. For performance estimation, the emphasis is on bit by bit maximum a-posteriori probability decoding, but message passing algorithms for certain codes have also been investigated. Considered here are single parity check codes, multidimensional product codes, and briefly, low-density parity-check codes. Several error rate results are presented for these various codes, together with performances of the simulation techniques
Mean Estimation from One-Bit Measurements
We consider the problem of estimating the mean of a symmetric log-concave
distribution under the constraint that only a single bit per sample from this
distribution is available to the estimator. We study the mean squared error as
a function of the sample size (and hence the number of bits). We consider three
settings: first, a centralized setting, where an encoder may release bits
given a sample of size , and for which there is no asymptotic penalty for
quantization; second, an adaptive setting in which each bit is a function of
the current observation and previously recorded bits, where we show that the
optimal relative efficiency compared to the sample mean is precisely the
efficiency of the median; lastly, we show that in a distributed setting where
each bit is only a function of a local sample, no estimator can achieve optimal
efficiency uniformly over the parameter space. We additionally complement our
results in the adaptive setting by showing that \emph{one} round of adaptivity
is sufficient to achieve optimal mean-square error
Decentralized Estimation over Orthogonal Multiple-access Fading Channels in Wireless Sensor Networks - Optimal and Suboptimal Estimators
Optimal and suboptimal decentralized estimators in wireless sensor networks
(WSNs) over orthogonal multiple-access fading channels are studied in this
paper. Considering multiple-bit quantization before digital transmission, we
develop maximum likelihood estimators (MLEs) with both known and unknown
channel state information (CSI). When training symbols are available, we derive
a MLE that is a special case of the MLE with unknown CSI. It implicitly uses
the training symbols to estimate the channel coefficients and exploits the
estimated CSI in an optimal way. To reduce the computational complexity, we
propose suboptimal estimators. These estimators exploit both signal and data
level redundant information to improve the estimation performance. The proposed
MLEs reduce to traditional fusion based or diversity based estimators when
communications or observations are perfect. By introducing a general message
function, the proposed estimators can be applied when various analog or digital
transmission schemes are used. The simulations show that the estimators using
digital communications with multiple-bit quantization outperform the estimator
using analog-and-forwarding transmission in fading channels. When considering
the total bandwidth and energy constraints, the MLE using multiple-bit
quantization is superior to that using binary quantization at medium and high
observation signal-to-noise ratio levels
On Stein's Identity and Near-Optimal Estimation in High-dimensional Index Models
We consider estimating the parametric components of semi-parametric multiple
index models in a high-dimensional and non-Gaussian setting. Such models form a
rich class of non-linear models with applications to signal processing, machine
learning and statistics. Our estimators leverage the score function based first
and second-order Stein's identities and do not require the covariates to
satisfy Gaussian or elliptical symmetry assumptions common in the literature.
Moreover, to handle score functions and responses that are heavy-tailed, our
estimators are constructed via carefully thresholding their empirical
counterparts. We show that our estimator achieves near-optimal statistical rate
of convergence in several settings. We supplement our theoretical results via
simulation experiments that confirm the theory
The equivalence of information-theoretic and likelihood-based methods for neural dimensionality reduction
Stimulus dimensionality-reduction methods in neuroscience seek to identify a
low-dimensional space of stimulus features that affect a neuron's probability
of spiking. One popular method, known as maximally informative dimensions
(MID), uses an information-theoretic quantity known as "single-spike
information" to identify this space. Here we examine MID from a model-based
perspective. We show that MID is a maximum-likelihood estimator for the
parameters of a linear-nonlinear-Poisson (LNP) model, and that the empirical
single-spike information corresponds to the normalized log-likelihood under a
Poisson model. This equivalence implies that MID does not necessarily find
maximally informative stimulus dimensions when spiking is not well described as
Poisson. We provide several examples to illustrate this shortcoming, and derive
a lower bound on the information lost when spiking is Bernoulli in discrete
time bins. To overcome this limitation, we introduce model-based dimensionality
reduction methods for neurons with non-Poisson firing statistics, and show that
they can be framed equivalently in likelihood-based or information-theoretic
terms. Finally, we show how to overcome practical limitations on the number of
stimulus dimensions that MID can estimate by constraining the form of the
non-parametric nonlinearity in an LNP model. We illustrate these methods with
simulations and data from primate visual cortex
Non-Data-Aided Parameter Estimation in an Additive White Gaussian Noise Channel
Non-data-aided (NDA) parameter estimation is considered for
binary-phase-shift-keying transmission in an additive white Gaussian noise
channel. Cramer-Rao lower bounds (CRLBs) for signal amplitude, noise variance,
channel reliability constant and bit-error rate are derived and it is shown how
these parameters relate to the signal-to-noise ratio (SNR). An alternative
derivation of the iterative maximum likelihood (ML) SNR estimator is presented
together with a novel, low complexity NDA SNR estimator. The performance of the
proposed estimator is compared to previously suggested estimators and the CRLB.
The results show that the proposed estimator performs close to the iterative ML
estimator at significantly lower computational complexity
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