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Content Provider | IEEE Xplore Digital Library |
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Author | Prasad, K. Rajan, B.S. |
Copyright Year | 2010 |
Abstract | A single-source network is said to be memory-free if all of the internal nodes (those except the source and the sinks) do not employ memory but merely send linear combinations of the incoming symbols (received at their incoming edges) on their outgoing edges. Memory-free networks with delay using network coding are forced to do inter-generation network coding, as a result of which the problem of some or all sinks requiring a large amount of memory for decoding is faced. In this work, we address this problem by utilizing memory elements at the internal nodes of the network also, which results in the reduction of the number of memory elements used at the sinks. We give an algorithm which employs memory at all the nodes of the network to achieve single- generation network coding. For fixed latency, our algorithm reduces the total number of memory elements used in the network to achieve single- generation network coding. We also discuss the advantages of employing single-generation network coding together with convolutional network-error correction codes (CNECCs) for networks with unit- delay and illustrate the performance gain of CNECCs by using memory at the intermediate nodes using simulations on an example network under a probabilistic network error model. |
Starting Page | 1 |
Ending Page | 6 |
File Size | 256119 |
Page Count | 6 |
File Format | |
ISBN | 9781424464029 |
ISSN | 19381883 |
e-ISBN | 9781424464043 |
DOI | 10.1109/ICC.2010.5502118 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2010-05-23 |
Publisher Place | South Africa |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Network coding Peer to peer computing Decoding Convolutional codes Network topology Encoding Communications Society Delay Performance gain Error correction codes |
Content Type | Text |
Resource Type | Article |
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