Classical
information theory ignores the issue of meaning for the engineering
problem of communication and assumes all information to be equally
improtant. In many situations however, all information is not created
equal. We investigate some fundamental limits on how much extra
protection can be provided to such high-priority information while
still communicating the low-priority information reliably.
Many
problems in information theory involve optimizing the
Kullback-Leibler divergence between probability distributions.
Differential geometry motivates a local approximation of the KL
divergence in terms of Euclidean distance. This Euclidean
approximation simplifies KL divergence optimizations into linear
algebra problems. Under this simplification, we solve the open
problem of broadcast with degraded message sets for very noisy
channels.
Using
graphical models in probability, we simplify the general problem of
broadcasting with degraded message sets. This additional structure
provides new insights for the general problem and solves it for a new
set of situations. A converse result is provided in terms of a
`mirror-image' of the actual network. The classical result for
the physically-degraded situation is a simple corollary of this
mirror-image converse.
Do
not fight with the channel randomness, exploit it! A scheme motivated
from dirty-paper coding is used for achieving capacity of a fading
channel with causal state information (CSI) at the transmitter. The
capacity per unit cost of a general channel with causal transmitter
CSI is also derived using similar scheme.
A self-contained geometric perspective to various error exponents in information theory. It uses an intuitive approach
based on a Pythagoras-like theorem for KL divergences. This shows the hidden geometry behind error exponents in
information theory and provides new insights into the nature of rare events.
Complete
list of
Publications(with
brief descriptions)