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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

Network Coding in Cooperative Communications: Friend or Foe?

TL;DR: It is shown that NC may not always benefit CC, and the important concept of network coding noise (NC noise) is introduced, which derives a closed-form expression for NC noise at each destination node and is shown to diminish the advantage of NC in CC.
Posted Content

An Equivalence between Network Coding and Index Coding

TL;DR: In this paper, it was shown that the network coding and index coding problems are equivalent in the general setting, which includes linear and non-linear codes, and an efficient reduction that maps a network coding instance to an index coding one while preserving feasibility was presented.
Proceedings ArticleDOI

List-decoding of subspace codes and rank-metric codes up to Singleton bound

TL;DR: A folded version of Gabidulin codes analogous to the folded Reed-Solomon codes of Guruswami and Rudra is introduced along with a list-decoding algorithm for such codes that achieves the information theoretic bound on the decoding radius of a rank-metric code.
Proceedings ArticleDOI

Feasibility of content dissemination between devices in moving vehicles

TL;DR: A protocol enabling devices in vehicles to identify and exchange content of shared interest, using the example of Personal Navigation Devices (PNDs), or SatNavs, is described.
Proceedings ArticleDOI

Tunable sparse network coding for multicast networks

TL;DR: A mechanism to perform efficient Gaussian elimination over sparse matrices going beyond belief propagation but maintaining low decoding complexity is presented.
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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