16 research outputs found
One-bit Distributed Sensing and Coding for Field Estimation in Sensor Networks
This paper formulates and studies a general distributed field reconstruction
problem using a dense network of noisy one-bit randomized scalar quantizers in
the presence of additive observation noise of unknown distribution. A
constructive quantization, coding, and field reconstruction scheme is developed
and an upper-bound to the associated mean squared error (MSE) at any point and
any snapshot is derived in terms of the local spatio-temporal smoothness
properties of the underlying field. It is shown that when the noise, sensor
placement pattern, and the sensor schedule satisfy certain weak technical
requirements, it is possible to drive the MSE to zero with increasing sensor
density at points of field continuity while ensuring that the per-sensor
bitrate and sensing-related network overhead rate simultaneously go to zero.
The proposed scheme achieves the order-optimal MSE versus sensor density
scaling behavior for the class of spatially constant spatio-temporal fields.Comment: Fixed typos, otherwise same as V2. 27 pages (in one column review
format), 4 figures. Submitted to IEEE Transactions on Signal Processing.
Current version is updated for journal submission: revised author list,
modified formulation and framework. Previous version appeared in Proceedings
of Allerton Conference On Communication, Control, and Computing 200
Fundamental limits of distributed tracking
Consider the following communication scenario. An n-dimensional source with memory is observed by K isolated encoders via parallel channels, who causally compress their observations to transmit to the decoder via noiseless rate-constrained links. At each time instant, the decoder receives K new codewords from the observers, combines them with the past received codewords, and produces a minimum- distortion estimate of the latest block of n source symbols. This scenario extends the classical one-shot CEO problem to multiple rounds of communication with communicators maintaining memory of the past.We prove a coding theorem showing that the minimum asymptotically (as n → ∞) achievable sum rate required to achieve a target distortion is equal to the directed mutual information from the observers to the decoder minimized subject to the distortion constraint and the separate encoding constraint. For the Gauss-Markov source observed via K parallel AWGN channels, we solve that minimal directed mutual information problem, thereby establishing the minimum asymptotically achievable sum rate. Finally, we explicitly bound the rate loss due to a lack of communication among the observers; that bound is attained with equality in the case of identical observation channels.The general coding theorem is proved via a new nonasymptotic bound that uses stochastic likelihood coders and whose asymptotic analysis yields an extension of the Berger-Tung inner bound to the causal setting. The analysis of the Gaussian case is facilitated by reversing the channels of the observers
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
Fundamental limits of distributed tracking
Consider the following communication scenario. An n-dimensional source with memory is observed by K isolated encoders via parallel channels, who causally compress their observations to transmit to the decoder via noiseless rate-constrained links. At each time instant, the decoder receives K new codewords from the observers, combines them with the past received codewords, and produces a minimum- distortion estimate of the latest block of n source symbols. This scenario extends the classical one-shot CEO problem to multiple rounds of communication with communicators maintaining memory of the past.We prove a coding theorem showing that the minimum asymptotically (as n → ∞) achievable sum rate required to achieve a target distortion is equal to the directed mutual information from the observers to the decoder minimized subject to the distortion constraint and the separate encoding constraint. For the Gauss-Markov source observed via K parallel AWGN channels, we solve that minimal directed mutual information problem, thereby establishing the minimum asymptotically achievable sum rate. Finally, we explicitly bound the rate loss due to a lack of communication among the observers; that bound is attained with equality in the case of identical observation channels.The general coding theorem is proved via a new nonasymptotic bound that uses stochastic likelihood coders and whose asymptotic analysis yields an extension of the Berger-Tung inner bound to the causal setting. The analysis of the Gaussian case is facilitated by reversing the channels of the observers
The Three-Terminal Interactive Lossy Source Coding Problem
The three-node multiterminal lossy source coding problem is investigated. We
derive an inner bound to the general rate-distortion region of this problem
which is a natural extension of the seminal work by Kaspi'85 on the interactive
two-terminal source coding problem. It is shown that this (rather involved)
inner bound contains several rate-distortion regions of some relevant source
coding settings. In this way, besides the non-trivial extension of the
interactive two terminal problem, our results can be seen as a generalization
and hence unification of several previous works in the field. Specializing to
particular cases we obtain novel rate-distortion regions for several lossy
source coding problems. We finish by describing some of the open problems and
challenges. However, the general three-node multiterminal lossy source coding
problem seems to offer a formidable mathematical complexity.Comment: New version with changes suggested by reviewers.Revised and
resubmitted to IEEE Transactions on Information Theory. 92 pages, 11 figures,
1 tabl
The relevance of communication theory for theories of representation
Prominent views about representation share a premise: that mathematical communication theory is blind to representational content. Here I challenge that premise by rejecting two common misconceptions: that Claude Shannon said that the meanings of signals are irrelevant for communication theory (he didn't and they aren't), and that since correlational measures can't distinguish representations from natural signs, communication theory can't distinguish them either (the premise is true but the conclusion is false; no valid argument can link them)
Distributed Reception in the Presence of Gaussian Interference
abstract: An analysis is presented of a network of distributed receivers encumbered by strong in-band interference. The structure of information present across such receivers and how they might collaborate to recover a signal of interest is studied. Unstructured (random coding) and structured (lattice coding) strategies are studied towards this purpose for a certain adaptable system model. Asymptotic performances of these strategies and algorithms to compute them are developed. A jointly-compressed lattice code with proper configuration performs best of all strategies investigated.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201