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Geometric Techniques in Multiterminal Communication and Estimation.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Geometric Techniques in Multiterminal Communication and Estimation./
作者:	Barnes, Leighton Pate.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	172 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-02, Section: B.
Contained By:	Dissertations Abstracts International83-02B.
標題:	Language. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28483296
ISBN:	9798505571798

Geometric Techniques in Multiterminal Communication and Estimation.
Barnes, Leighton Pate.

Geometric Techniques in Multiterminal Communication and Estimation. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 172 p.

Source: Dissertations Abstracts International, Volume: 83-02, Section: B.

Thesis (Ph.D.)--Stanford University, 2021.

This item must not be sold to any third party vendors.

Since its inception in 1948, one of the main goals in information theory has been to extend its original scope of point-to-point communication to include networks of nodes exchanging information. Towards this goal, we develop tools from high-dimensional geometry to analyze the fundamental limits of communication and statistical estimation tasks in a networked setting.In the first half of the thesis, we describe a problem in network communications -- the relay channel -- and prove a new upper bound on the capacity of this channel which resolves an open problem posed by Thomas Cover in "Open Problems in Communication and Computation", Springer-Verlag, 1987. The proof is highly geometric, with its main ingredient being a new isoperimetric result on high-dimensional spheres that builds on a Riesz-type rearrangement inequality.In the second half of the thesis, we consider a collection of networked statistical estimation problems modeling bandwidth and privacy constraints in distributed and federated learning systems. In these problems, data is distributed across many nodes in a network and must be communicated to a centralized estimator under communication, privacy, or mutual information constraints. We show how a geometric interpretation of Fisher information from the processed statistical samples can derive tight minimax lower bounds for many distributed estimation problems of interest.

ISBN: 9798505571798Subjects--Topical Terms:

643551
Language.

Geometric Techniques in Multiterminal Communication and Estimation.
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