Abstract
dc:description.abstractAdversarial network coding studies the transmission of data over networks affected by adversarial noise. In this realm, the noise is modeled by an omniscient adversary who is restricted to corrupting a proper subset of the network edges. In 2018, Ravagnani and Kschischang established a combinatorial framework for adversarial networks. The study was recently furthered by Beemer, Kilic and Ravagnani, with particular focus on the one-shot capacity: a measure of the maximum number of symbols that can be transmitted in a single use of the network without errors. In this thesis, both bounds and capacity-achieving schemes are provided for families of adversarial networks in multiple transmission rounds. We also demonstrate scenarios where we transmit more information using a network multiple times for communication versus using the network once. Some results in this thesis are joint work with Giuseppe Cotardo (Virginia Tech), Gretchen Matthews (Virginia Tech) and Alberto Ravagnani (Eindhoven University of Technology).
Degree
thesis:*- Name thesis:degree_name
- Master of Science
- Level thesis:degree_level
- masters
- Discipline thesis:degree_discipline
- Mathematics
- Department dc:contributor.department
- Mathematics
- Grantor dc:publisher
- Virginia Tech
- Year dc:date.issued
- 2024
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Shapiro, Julia Marie
- Chairs dc:contributor.committeechair
-
- Matthews, Gretchen L.
- Lopez Valdez, Hiram Habid
- Committee member dc:contributor.committeemember
-
- Shimozono, Mark M.
Subjects
dc:subject × 3Rights
dc:rights- Statement dc:rights
-
- In Copyright
- Licence dc:rights.uri
- Language dc:language.iso
- en
Identifiers
dc:identifier.*- Dc Identifier Other
- vt_gsexam:40007
- OAI identifier oai:identifier
- oai:vtechworks.lib.vt.edu:10919/118924