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Edge-Cut Bounds on Network Coding Rates

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TLDR
A new bound on communication rates is developed that applies to network coding, which is a promising active network application that has processors transmit packets that are general functions, for example a bit-wise XOR of selected received packets.
Abstract
Active networks are network architectures with processors that are capable of executing code carried by the packets passing through them. A critical network management concern is the optimization of such networks and tight bounds on their performance serve as useful design benchmarks. A new bound on communication rates is developed that applies to network coding, which is a promising active network application that has processors transmit packets that are general functions, for example a bit-wise XOR, of selected received packets. The bound generalizes an edge-cut bound on routing rates by progressively removing edges from the network graph and checking whether certain strengthened d-separation conditions are satisfied. The bound improves on the cut-set bound and its efficacy is demonstrated by showing that routing is rate-optimal for some commonly cited examples in the networking literature.

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Network Coding and Matroid Theory This paper explores the connection between network coding and matroid theory, a branch of mathematics that generalizes linear algebra and graph theory.

TL;DR: This tutorial paper reviews many con- nections between matroids and network coding theory, with specific emphasis on network solvability, admissible network alphabet sizes, linear coding, and network capacity.
Proceedings ArticleDOI

The encoding complexity of network coding with two simple multicast sessions

TL;DR: It is proved that the number of encoding links required to achieve a network coding solution is upper-bounded by max{2,⌊(√2N-7/4)+1/2⌋}.

Modern Low-Complexity Capacity-Achieving Codes For Network Communication - eScholarship

Naveen Goela
TL;DR: In this article, the authors considered the problem of designing capacity-achieving network codes realizable by modern signal processing circuits and proposed a vector-space function alignment scheme inspired by the concept of interference alignment for channel communications.
Posted Content

Multicut Lower Bounds via Network Coding

TL;DR: A new technique to certify lower bounds on the multicut size using network coding, which identifies a class of networks on which the rate is equal to the size of the minimum multicut and shows this class is closed under the strong graph product.
Proceedings ArticleDOI

Multicut lower bounds via network coding

TL;DR: In this article, the authors introduced a new technique to certify lower bounds on the multicut size using network coding and showed that the problem is closed under the strong graph product, where the rate is equal to the size of the minimum multicut.
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.
Book

Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference

TL;DR: Probabilistic Reasoning in Intelligent Systems as mentioned in this paper is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty, and provides a coherent explication of probability as a language for reasoning with partial belief.
Journal ArticleDOI

Network information flow

TL;DR: 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.
Book

Flows in networks

TL;DR: Ford and Fulkerson as mentioned in this paper set the foundation for the study of network flow problems and developed powerful computational tools for solving and analyzing network flow models, and also furthered the understanding of linear programming.
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