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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Bibliographic Details
Published in:	IEEE transactions on information theory Vol. 52; no. 10; pp. 4413 - 4430
Main Authors:	Ho, T., Medard, M., Koetter, R., Karger, D.R., Effros, M., Jun Shi, Leong, B.
Format:	Journal Article
Language:	English
Published:	New York, NY IEEE 01.10.2006 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	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
Online Access:	Get full text
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Abstract	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
AbstractList	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 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. [PUBLICATION ABSTRACT]
Author	Jun Shi Effros, M. Karger, D.R. Ho, T. Koetter, R. Medard, M. Leong, B.
Author_xml	– sequence: 1 givenname: T. surname: Ho fullname: Ho, T. organization: Lab. for Inf. & Decision Syst., MIT, Cambridge, MA – sequence: 2 givenname: M. surname: Medard fullname: Medard, M. – sequence: 3 givenname: R. surname: Koetter fullname: Koetter, R. – sequence: 4 givenname: D.R. surname: Karger fullname: Karger, D.R. – sequence: 5 givenname: M. surname: Effros fullname: Effros, M. – sequence: 6 surname: Jun Shi fullname: Jun Shi – sequence: 7 givenname: B. surname: Leong fullname: Leong, B.
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Cites_doi	10.1007/BF02579407 10.1109/TIT.2002.807285 10.1145/312203.312207 10.1109/IWWAN.2004.1525570 10.1109/TIT.1982.1056524 10.1017/CBO9780511814075 10.1109/ISIT.2003.1228384 10.1109/TNET.2003.815302 10.1109/JSAC.2003.818801 10.1109/TIT.2006.881746 10.1109/18.850663 10.1109/ISIT.2003.1228459 10.1109/INFCOM.2005.1498443 10.1109/TIT.2004.834751 10.1109/TIT.2006.874523 10.1109/TNET.2003.818197 10.1504/IJSNET.2008.019248 10.1109/TIT.2008.2009809 10.1145/570739.570741 10.1109/TIT.1973.1055037 10.1109/TIT.2005.864435 10.1109/TIT.2005.847712 10.1109/ISIT.2003.1228458
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SubjectTerms	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
Title	A Random Linear Network Coding Approach to Multicast
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