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Journal ArticleDOI

Network information flow

TLDR
This work reveals that it is in general not optimal to regard the information to be multicast as a "fluid" which can simply be routed or replicated, and by employing coding at the nodes, which the work refers to as network coding, bandwidth can in general be saved.
Abstract
We introduce a new class of problems called network information flow which is inspired by computer network applications. Consider a point-to-point communication network on which a number of information sources are to be multicast to certain sets of destinations. We assume that the information sources are mutually independent. The problem is to characterize the admissible coding rate region. This model subsumes all previously studied models along the same line. We study the problem with one information source, and we have obtained a simple characterization of the admissible coding rate region. Our result can be regarded as the max-flow min-cut theorem for network information flow. Contrary to one's intuition, our work reveals that it is in general not optimal to regard the information to be multicast as a "fluid" which can simply be routed or replicated. Rather, by employing coding at the nodes, which we refer to as network coding, bandwidth can in general be saved. This finding may have significant impact on future design of switching systems.

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Citations
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Journal ArticleDOI

Optimality and Approximate Optimality of Source-Channel Separation in Networks

TL;DR: It is shown that the separation approach is optimal in two general scenarios and is approximately optimal in a third scenario, which generalizes the second scenario by allowing each source to be reconstructed at multiple destinations with different distortions.
Book

A Primer on Physical-Layer Network Coding

TL;DR: A goal of this book/course is to allow the reader to gain a deeper appreciation of the various nuances of wireless communications and networking by focusing on problems arising from the study of PNC.
Patent

System and method for decentralized autonomous healthcare economy platform

TL;DR: In this paper, a system and method for a decentralized autonomous healthcare economy platform is presented, which aggregates all of the healthcare data into a global graph-theoretic topology and processes the data via a hybrid federated and peer-to-peer distributed processing architectures.
Proceedings ArticleDOI

Compressed network coding vectors

TL;DR: This work presents a third approach to employ compressed coding vectors to efficiently convey the coding coefficients, without altering the operation of intermediate network nodes, and develops optimal designs for such vectors.
Journal ArticleDOI

Weight properties of network codes

TL;DR: The results show that if the weight measure on the error vectors is the Hamming weight, the capability of a linear code is fully characterised by a single minimum distance, while for a nonlinear code, two different minimum distances are needed for characterising the capabilities of the code for error correction and for errordetection.
References
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Book

Elements of information theory

TL;DR: The author examines the role of entropy, inequality, and randomness in the design of codes and the construction of codes in the rapidly changing environment.
Journal ArticleDOI

Factor graphs and the sum-product algorithm

TL;DR: A generic message-passing algorithm, the sum-product algorithm, that operates in a factor graph, that computes-either exactly or approximately-various marginal functions derived from the global function.
Journal ArticleDOI

Noiseless coding of correlated information sources

TL;DR: The minimum number of bits per character R_X and R_Y needed to encode these sequences so that they can be faithfully reproduced under a variety of assumptions regarding the encoders and decoders is determined.
Journal ArticleDOI

Linear network coding

TL;DR: This work forms this multicast problem and proves that linear coding suffices to achieve the optimum, which is the max-flow from the source to each receiving node.
Journal ArticleDOI

Achievable rates for multiple descriptions

TL;DR: These rates are shown to be optimal for deterministic distortion measures for random variables and Shannon mutual information.
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