Friday, 11:30 - 11:55 h, Room: MA 004

 

Dimitris Bertsimas
Network information theory via robust optimization

Coauthor: Chaitanya Bandi

 

Abstract:
We present a robust optimization framework to
solve the central problem of network information theory
of characterizing the capacity region and constructing matching optimal
codes for multi-user channels with interference. We first formulate the single user Gaussian channel as a semidefinite optimization problem with rank one constraints and recover the known capacity region (Shannon-1948) and
construct a matching optimal code. We then
characterize the capacity regions of the multi-user Gaussian interference
channel, the multicast and the multi-access Gaussian channels and
construct matching optimal codes by solving semidefinite optimization
problems with rank one constraints. We report numerical results that
show that our proposed approach is numerically tractable for code-book
sizes of up to 100,000 codewords. We further examine how the probability
description of noise affects the nature of the corresponding optimization
problem and show that for the case of exponential channels the optimization problem becomes a binary, mixed linear optimization problem that can be solved by commercial solvers.

 

Talk 3 of the invited session Fri.1.MA 004
"A robust optimization approach to stochastic analysis" [...]
Cluster 20
"Robust optimization" [...]

 

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