156 research outputs found
On optimum parameter modulation-estimation from a large deviations perspective
We consider the problem of jointly optimum modulation and estimation of a
real-valued random parameter, conveyed over an additive white Gaussian noise
(AWGN) channel, where the performance metric is the large deviations behavior
of the estimator, namely, the exponential decay rate (as a function of the
observation time) of the probability that the estimation error would exceed a
certain threshold. Our basic result is in providing an exact characterization
of the fastest achievable exponential decay rate, among all possible
modulator-estimator (transmitter-receiver) pairs, where the modulator is
limited only in the signal power, but not in bandwidth. This exponential rate
turns out to be given by the reliability function of the AWGN channel. We also
discuss several ways to achieve this optimum performance, and one of them is
based on quantization of the parameter, followed by optimum channel coding and
modulation, which gives rise to a separation-based transmitter, if one views
this setting from the perspective of joint source-channel coding. This is in
spite of the fact that, in general, when error exponents are considered, the
source-channel separation theorem does not hold true. We also discuss several
observations, modifications and extensions of this result in several
directions, including other channels, and the case of multidimensional
parameter vectors. One of our findings concerning the latter, is that there is
an abrupt threshold effect in the dimensionality of the parameter vector: below
a certain critical dimension, the probability of excess estimation error may
still decay exponentially, but beyond this value, it must converge to unity.Comment: 26 pages; Submitted to the IEEE Transactions on Information Theor
The Likelihood Encoder for Lossy Compression
A likelihood encoder is studied in the context of lossy source compression.
The analysis of the likelihood encoder is based on the soft-covering lemma. It
is demonstrated that the use of a likelihood encoder together with the
soft-covering lemma yields simple achievability proofs for classical source
coding problems. The cases of the point-to-point rate-distortion function, the
rate-distortion function with side information at the decoder (i.e. the
Wyner-Ziv problem), and the multi-terminal source coding inner bound (i.e. the
Berger-Tung problem) are examined in this paper. Furthermore, a non-asymptotic
analysis is used for the point-to-point case to examine the upper bound on the
excess distortion provided by this method. The likelihood encoder is also
related to a recent alternative technique using properties of random binning
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