Network information flow

By R. Ahlswede, Ning Cai, Shuo-Yen Robert Li, Raymond W. Yeung. IEEE Transactions on Information Theory, 2000

8,519 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/387588025d7b09aa8245308576b31af850a93a10

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In Computational Theory and Mathematics

In Computer Networks and Communications

Citations in the corpus

Network Coding for Distributed Storage Systems (2007, background): “A number of recent studies [4], [18], [7], [21], [2], [27] have designed and evaluated large-scale, peer-to-peer distributed storage systems.”
Information fusion for wireless sensor networks: Methods, models, and classifications (2007, methodology): “Zhang and Wicker [2005] provide a framework for the design and analysis of distributed, joint source and network coding [Ahlswede et al. 2000] algorithms that optimize the tradeoff between compression ef.ciency and network robustness.”
Network coding for large scale content distribution (2005, background): “Network coding is a novel mechanism proposed to improve the throughput utilization of a given network topology [3].”
Analyzing the download time of availability codes (2015, methodology): “We call the FJ queue for this particular code as the butterfly queue, since the code resembles the one used over the butterfly network for network coding [1].”
Towards an information theory of large networks: an achievable rate region (2001, background): “It is shown in [2] that such coding strictly increases the achievable rate in multicasting, i.”
The Capacity of Robust Private Information Retrieval With Colluding Databases (2016, background): “2777490 connections between locally decodable and locally recoverable codes [12], PIR also connects to distributed data storage repair [13] and index coding [14], which in turn encompass all of network coding [15].”
Growth codes: maximizing sensor network data persistence (2006, background): “Distributed encoding schemes that aim to recover all data permit nodes to re-code data en-route to the destination have also been shown to be a more efficient means of communication [27].”
Decentralized erasure codes for distributed networked storage (2006, background): “In random linear coding, every element in the generator matrix of the code is selected independently and uniformly from a ﬁnite ﬁeld F q .”
A Fundamental Tradeoff Between Computation and Communication in Distributed Computing (2016, background): “, [29]–[31]) is sufficient to implement any multicast communication where messages are intended by all receivers, it is generally sub-optimal for index coding problems where every client requests different messages.”
Analyzing and Improving a BitTorrent Networks Performance Mechanisms (2006, background): “They go on to propose a network coding [10] based scheme called Avalanche that alleviates these problems.”
Computing Linear Functions by Linear Coding Over Networks (2011, background): “[1] is a special case of network computing in which the function to be computed at the receivers corresponds to the set of the source messages.”
Optimized constellations for two-way wireless relaying with physical network coding (2009, background): “W IRELESS network coding has recently received a lot of attention in research community, although the concept of network coding has been around for almost a decade [2].”
Wireless Networks With RF Energy Harvesting: A Contemporary Survey (2014, background): “Network coding [231] is well-known to be energy efficient in information transmission.”
Byzantine modification detection in multicast networks using randomized network coding (2004, background): “Interest in network coding has grown following demonstrations of its various advantages: in network capacity [1], robustness to nonergodic network failures [2] and ergodic packet erasures [3], [4], and distributed network operation [5].”
Optimal Estimation in Networked Control Systems Subject to Random Delay and Packet Drop (2008, background): “Obviously, packet loss and delay can be reduced by network coding [ 9 ], distributed signal processing [10], in-network data compression [11], and packet routing protocols [12], but not completely avoided due to inherent unreliable nature of wireless communication.”