scispace - formally typeset
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.

read more

Citations
More filters
Proceedings ArticleDOI

Network coding for efficient communication in extreme networks

TL;DR: A communication algorithm is proposed that significantly reduces the overhead of probabilistic routing algorithms, making it a suitable building block for a delay-tolerant network architecture and shows by simulation that this algorithm achieves the reliability and robustness of flooding at a small fraction of the overhead.
Journal ArticleDOI

Insufficiency of linear coding in network information flow

TL;DR: It is shown that the network coding capacity of this counterexample network is strictly greater than the maximum linear coding capacity over any finite field, so the network is not even asymptotically linearly solvable.

The Importance of Being Opportunistic: Practical Network Coding for Wireless Environments

TL;DR: The results show that COPE substantially improves the network throughput, and as the number of flows and the contention level increases, COPE’s throughput becomes many times higher than current 802.11 mesh networks.
Journal ArticleDOI

Capacity of the Gaussian Two-Way Relay Channel to Within ${1\over 2}$ Bit

TL;DR: An achievable scheme composed of nested lattice codes for the uplink and structured binning for the downlink based on a three-stage lattice partition chain, which is a key ingredient for producing the best gap-to-capacity results to date.
Proceedings ArticleDOI

Wireless diversity through network coding

TL;DR: The results show that network-coded DAS leads to better diversity performance as compared to conventional DAS, at a lower hardware cost and higher spectral efficiency.
References
More filters
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.
Related Papers (5)