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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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Citations
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Journal ArticleDOI

Network Coding in Node-Constrained Line and Star Networks

TL;DR: Line and star networks with both node and edge constraints are studied in the network coding framework and a simple linear coding scheme is proposed which is based on the combinatorial optimization problem of cycle packing in directed graphs.
Proceedings ArticleDOI

Progressive d-separating edge set bounds on network coding rates

TL;DR: A bound on network coding rates is developed that generalizes an edge-cut bound on routing rates and is demonstrated by showing that routing is rate-optimal for some commonly cited examples in the networking literature.
Posted Content

A Theory of Network Equivalence, Parts I and II

TL;DR: This work derives general conditions under which a network of noiseless bit pipes is an upper or lower bounding model for a multiterminal channel and bounds on the accuracy of network capacity bounds derived using those models are shown.
Journal ArticleDOI

Lower bounds on the maximum energy benefit of network coding for wireless multiple unicast

TL;DR: Lower bounds on the benefit of network coding are established, defined as the maximum of the ratio of the minimum energy required by routing and network coding solutions, where the maximum is over all configurations.
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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