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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 in undirected graphs is either very helpful or not helpful at all

TL;DR: In this paper, the authors show that even a small advantage over MCF can be amplified to yield a near-maximum possible gap, which is close to the best currently known upper bound, which follows from the connection between MCF and sparsest cuts.
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Alignment based Network Coding for Two-Unicast-Z Networks

TL;DR: In this article, a linear network coding algorithm for two-unicast-Z networks over directed acyclic graphs is presented, with interference alignment as one of its key ingredients.
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Generalized Cut-Set Bounds for Broadcast Networks

TL;DR: In this paper, a new set of network coding bounds for general broadcast networks is proposed, which combine the basic cuts of the network via a variety of set operations (not just the union) and are established via the submodularity of Shannon entropy.
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Beyond the Cut-Set Bound: Uncertainty Computations in Network Coding with Correlated Sources

TL;DR: In this paper, the authors introduce a new technique for proving converses for the problem of transmission of correlated sources in networks, that results in bounds that are tighter than the corresponding cut-set bounds.
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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