A Random Linear Network Coding Approach to Multicast

We present a distributed random linear network coding approach for transmission and compression of information in general multisource multicast networks. Network nodes independently and randomly select linear mappings from inputs onto output links over some field. We show that this achieves capacity...

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Veröffentlicht in:	IEEE transactions on information theory Jg. 52; H. 10; S. 4413 - 4430
Hauptverfasser:	Ho, T., Medard, M., Koetter, R., Karger, D.R., Effros, M., Jun Shi, Leong, B.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York, NY IEEE 01.10.2006 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Applied sciences Artificial intelligence Associate members Capacity planning Codes Coding Coding, codes Communications networks Compressing Computer networks Computer science Data compression Data transmission Derivation Distributed compression distributed networking Exact sciences and technology Information, signal and communications theory Laboratories Links Multicast Network coding Networks random linear coding Robustness Routing Signal and communications theory Systems, networks and services of telecommunications Telecommunications Telecommunications and information theory Transmission and modulation (techniques and equipments) Vectors network coding Data compression Mapping Distributed compression Random coding Failures Data broadcast Information dissemination Nodes random linear coding Multicast Linear coding Decentralized system Network routing Robustness distributed networking
ISSN:	0018-9448, 1557-9654
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Zusammenfassung:	We present a distributed random linear network coding approach for transmission and compression of information in general multisource multicast networks. Network nodes independently and randomly select linear mappings from inputs onto output links over some field. We show that this achieves capacity with probability exponentially approaching 1 with the code length. We also demonstrate that random linear coding performs compression when necessary in a network, generalizing error exponents for linear Slepian-Wolf coding in a natural way. Benefits of this approach are decentralized operation and robustness to network changes or link failures. We show that this approach can take advantage of redundant network capacity for improved success probability and robustness. We illustrate some potential advantages of random linear network coding over routing in two examples of practical scenarios: distributed network operation and networks with dynamically varying connections. Our derivation of these results also yields a new bound on required field size for centralized network coding on general multicast networks
Bibliographie:	SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-2 content type line 23
ISSN:	0018-9448 1557-9654
DOI:	10.1109/TIT.2006.881746