Asymptotic analysis for multi-user channels

This dissertation studies the asymptotics of two multi-user channel problems. The fingerprinting channel is associated with digital fingerprinting, which is an emerging technology to protect multimedia from unauthorized redistribution. The encoder embeds fingerprints into a host sequence and provides the decoder with the capability to trace back pirated copies to the colluders. The multiple access channel (MAC) is a classical problem in the field of network information theory. Multiple senders cooperate with one another to maximize their rates of communication to a single receiver. We address the problem of asymptotic analysis when the size of the problem goes to infinity. The fundamental metric of measuring the detection capability of a fingerprinting system is capacity. It has recently been derived as the limit value of a sequence of maximin games with mutual information as their payoff functions. However, these games generally do not admit saddle-point solutions and are very hard to solve. Here under a modified version of the combined digit model proposed by Skoric et al., we reformulate the capacity as the value of a single two-person zero-sum game, and show that it is achieved by a saddle-point solution. For fingerprinting capacity games with k pirates, we provide capacities along with optimal strategies for both players of the game when k is small. For large k, we show that capacity is asymptotic to A/k^2 where the constant A is specified as the maximin value of a continuous functional game. Saddle-point solutions to the game are obtained using methods of variational calculus. For multiple access channels we study the maximum achievable rate region for a given blocklength n and a desired error probability epsilon. The inner region for the discrete memoryless MAC is approximated by a single-lettered expression I-(1/sqrt(n))*Q_inv(V,epsilon) where I is associated with the capacity pentagon bounds by Ahlswede and Liao, V is the MAC dispersion matrix, and Q_inv is the inverse complementary multivariate Gaussian cumulative distribution region. For outer regions, we provide general converse bounds for both average error probability and maximal error probability criteria.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2014

Author dc:creator

• Huang, Yen-Wei

Contributors dc:contributor

• Moulin, Pierre
• Basar, Tamer
• Hajek, Bruce
• Veeravalli, Venugopal V.
• Milenkovic, Olgica

• Multi-User Channel
• Fingerprinting
• Traitor Tracing
• Game Theory
• Minimax Analysis
• Asymptotic Analysis
• Multiple Access Channel
• Finite Blocklength Coding

Statement dc:rights

• Copyright 2013 Yen-Wei Huang

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/46663

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/46663