Reliability of Broadcast Communications Under Sparse Random Linear Network Coding
Ultrareliable point-to-multipoint communications are expected to become pivotal in networks offering future dependable services for smart cities. In this regard, sparse random linear network coding techniques have been widely employed to provide an efficient way to improve the reliability of broadca...
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| Vydáno v: | IEEE transactions on vehicular technology Ročník 67; číslo 5; s. 4677 - 4682 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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New York
IEEE
01.05.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 0018-9545, 1939-9359 |
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| Abstract | Ultrareliable point-to-multipoint communications are expected to become pivotal in networks offering future dependable services for smart cities. In this regard, sparse random linear network coding techniques have been widely employed to provide an efficient way to improve the reliability of broadcast and multicast data streams. This paper addresses the pressing concern of providing a tight approximation to the probability of a user recovering a data stream protected by this kind of coding technique. In particular, by exploiting the Stein-Chen method, we provide a novel and general performance framework applicable to any combination of system and service parameters, such as finite field sizes, lengths of the data stream, and level of sparsity. The deviation of the proposed approximation from Monte Carlo simulations is negligible, improving significantly on the state-of-the-art performance bounds. |
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| AbstractList | Ultrareliable point-to-multipoint communications are expected to become pivotal in networks offering future dependable services for smart cities. In this regard, sparse random linear network coding techniques have been widely employed to provide an efficient way to improve the reliability of broadcast and multicast data streams. This paper addresses the pressing concern of providing a tight approximation to the probability of a user recovering a data stream protected by this kind of coding technique. In particular, by exploiting the Stein-Chen method, we provide a novel and general performance framework applicable to any combination of system and service parameters, such as finite field sizes, lengths of the data stream, and level of sparsity. The deviation of the proposed approximation from Monte Carlo simulations is negligible, improving significantly on the state-of-the-art performance bounds. |
| Author | Brown, Suzie Johnson, Oliver Tassi, Andrea |
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| SubjectTerms | Approximation broadcast communication Coding Computer simulation Data transmission Decoding Encoding Fields (mathematics) Junctions Mathematical analysis Monte Carlo methods Multicast multicast communications Network coding Network reliability Reliability Sparse matrices Sparse random network coding State of the art Stein–Chen method |
| Title | Reliability of Broadcast Communications Under Sparse Random Linear Network Coding |
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