Network coding for delay challenged environments

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 191-196).
Delay is a fundamental problem of data communication and networks, a problem that is not usually addressed in classical coding, information or networking theory. We focus on the general problem of delay challenged networks. This delay challenge may be related to different reasons, for example, 1) large latency, which can affect the performance of the system in delay, throughput or energy efficiency, 2) half-duplex constraints on the nodes, which precludes a node to receive and transmit at the same time, and/or 3) application-level requirements for reliable, fast and efficient dissemination of information. We consider three main problems of study and the role of network coding on solving these problems. The first is related to the problem of reliable communication in time-division duplexing channels, also known as half-duplex channels, in the presence of large latency. In large latency channels, feedback about received packets may lag considerably the transmission of the original packets, limiting the feedback's usefulness. Moreover, the time duplex constraints may entail that receiving feedback may be costly. In this work, we consider tailoring feedback and (network) coding jointly in such settings to reduce the mean delay for successful in order reception of packets. We find that, in certain applications, judicious choices provide results that are close to those that would be obtained with a full-duplex system. The second part of this thesis studies the problem of data dissemination in arbitrary networks. In particular, we study the problem of minimizing the delay incurred in disseminating a finite number of data packets. We show that the optimal solution to the problem can be thought of as a scheduling problem, which is hard to solve. Thus, we consider the use of a greedy linear network coding algorithm that only takes into account the current state of the system to make a decision. The proposed algorithm tries to maximize the impact on the network at each slot, i
by Daniel Enrique Lucani.
Ph.D.