2 research outputs found
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
Distributed estimation in multi-agent networks
A problem of distributed state estimation at multiple agents that are
physically connected and have competitive interests is mapped to a distributed
source coding problem with additional privacy constraints. The agents interact
to estimate their own states to a desired fidelity from their (sensor)
measurements which are functions of both the local state and the states at the
other agents. For a Gaussian state and measurement model, it is shown that the
sum-rate achieved by a distributed protocol in which the agents broadcast to
one another is a lower bound on that of a centralized protocol in which the
agents broadcast as if to a virtual CEO converging only in the limit of a large
number of agents. The sufficiency of encoding using local measurements is also
proved for both protocols.Comment: Alternate title: Interactive Source Coding with Privacy Constraints;
presented at the IEEE Intl. Symp. Information Theory 201