DocumentCode :
3607256
Title :
Euclidean Information Theory of Networks
Author :
Shao-Lun Huang ; Changho Suh ; Lizhong Zheng
Author_Institution :
Res. Lab. of Electron., Massachusetts Inst. of Technol., Cambridge, MA, USA
Volume :
61
Issue :
12
fYear :
2015
Firstpage :
6795
Lastpage :
6814
Abstract :
In this paper, we extend the information theoretic framework that was developed in earlier works to multi-hop network settings. For a given network, we construct a novel deterministic model that quantifies the ability of the network in transmitting private and common messages across users. Based on this model, we formulate a linear optimization problem that explores the throughput of a multi-layer network, thereby offering the optimal strategy as to what kind of common messages should be generated in the network to maximize the throughput. With this deterministic model, we also investigate the role of feedback for multi-layer networks, from which we identify a variety of scenarios in which feedback can improve transmission efficiency. Our results provide fundamental guidelines as to how to coordinate cooperation between users to enable efficient information exchanges across them.
Keywords :
information theory; linear programming; Euclidean information theoretic framework; linear optimization problem; novel deterministic model; transmission efficiency; Approximation methods; Couplings; Optimization; Receivers; Spread spectrum communication; Throughput; Deterministic Model; Divergence Transition Matrix (DTM); Feedback; Kullback-Leibler Divergence Approximation; Kullback-Leibler divergence approximation; Linear Information Coupling (LIC) Problem; Linear information coupling (LIC) problem; deterministic model; divergence transition matrix (DTM); feedback;
fLanguage :
English
Journal_Title :
Information Theory, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9448
Type :
jour
DOI :
10.1109/TIT.2015.2484066
Filename :
7283641
Link To Document :
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