A Matter of Perspective: Reliable Communication and Coping with Interference with Only Local Views

dc.contributor.advisorSabharwal, Ashutosh
dc.contributor.committeeMemberAazhang, Behnaam
dc.contributor.committeeMemberKnightly, Edward W.
dc.contributor.committeeMemberTapia, Richard A.
dc.contributor.committeeMemberChiang, Mung
dc.creatorKao, David
dc.date.accessioned2012-09-06T04:36:16Z
dc.date.accessioned2012-09-06T04:36:20Z
dc.date.available2012-09-06T04:36:16Z
dc.date.available2012-09-06T04:36:20Z
dc.date.created2012-05
dc.date.issued2012-09-05
dc.date.submittedMay 2012
dc.date.updated2012-09-06T04:36:20Z
dc.description.abstractThis dissertation studies interference in wireless networks. Interference results from multiple simultaneous attempts to communicate, often between unassociated sources and receivers, preventing extensive coordination. Moreover, in practical wireless networks, learning network state is inherently expensive, and nodes often have incomplete and mismatched views of the network. The fundamental communication limits of a network with such views is unknown. To address this, we present a local view model which captures asymmetries in node knowledge. Our local view model does not rely on accurate knowledge of an underlying probability distribution governing network state. Therefore, we can make robust statements about the fundamental limits of communication when the channel is quasi-static or the actual distribution of state is unknown: commonly faced scenarios in modern commercial networks. For each local view, channel state parameters are either perfectly known or completely unknown. While we propose no mechanism for network learning, a local view represents the result of some such mechanism. We apply the local view model to study the two-user Gaussian interference channel: the smallest building block of any interference network. All seven possible local views are studied, and we find that for five of the seven, there exists no policy or protocol that universally outperforms time-division multiplexing (TDM), justifying the orthogonalized approach of many deployed systems. For two of the seven views, TDM-beating performance is possible with use of opportunistic schemes where opportunities are revealed by the local view. We then study how message cooperation --- either at transmitters or receivers --- increases capacity in the local view two-user Gaussian interference channel. The cooperative setup is particularly appropriate for modeling next-generation cellular networks, where costs to share message data among base stations is low relative to costs to learn channel coefficients. For the cooperative setting, we find: (1) opportunistic approaches are still needed to outperform TDM, but (2) opportunities are more abundant and revealed by more local views. For all cases studied, we characterize the capacity region to within some known gap, enabling computation of the generalized degrees of freedom region, a visualization of spatial channel resource usage efficiency.
dc.format.mimetypeapplication/pdf
dc.identifier.citationKao, David. "A Matter of Perspective: Reliable Communication and Coping with Interference with Only Local Views." (2012) Diss., Rice University. <a href="https://hdl.handle.net/1911/64691">https://hdl.handle.net/1911/64691</a>.
dc.identifier.slug123456789/ETD-2012-05-165
dc.identifier.urihttps://hdl.handle.net/1911/64691
dc.language.isoeng
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.
dc.subjectWireless networks
dc.subjectInterference
dc.subjectDistributed systems
dc.subjectInformation theory
dc.subjectLocal views
dc.subjectRobust systems
dc.titleA Matter of Perspective: Reliable Communication and Coping with Interference with Only Local Views
dc.typeThesis
dc.type.materialText
thesis.degree.departmentElectrical and Computer Engineering
thesis.degree.disciplineEngineering
thesis.degree.grantorRice University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy
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