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Capacity of wireless erasure networks

Dana, A.F., Gowaikar, R., Palanki, R., Hassibi, B., Effros, M.·2006·IEEE Transactions on Information Theory

Abstract

In this paper, a special class of wireless networks, called wireless erasure networks, is considered. In these networks, each node is connected to a set of nodes by possibly correlated erasure channels. The network model incorporates the broadcast nature of the wireless environment by requiring each node to send the same signal on all outgoing channels. However, we assume there is no interference in reception. Such models are therefore appropriate for wireless networks where all information transmission is packetized and where some mechanism for interference avoidance is already built in. This paper looks at multicast problems over these networks. The capacity under the assumption that erasure locations on all the links of the network are provided to the destinations is obtained. It turns out that the capacity region has a nice max-flow min-cut interpretation. The definition of cut-capacity in these networks incorporates the broadcast property of the wireless medium. It is further shown that linear coding at nodes in the network suffices to achieve the capacity region. Finally, the performance of different coding schemes in these networks when no side information is available to the destinations is analyzed

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Metadata

Authors
Dana, A. F., Gowaikar, R., Palanki, R., Hassibi, B., & Effros, M.
Venue
IEEE Transactions on Information Theory
Published
2006-03
Volume
52
Issue
3
Pages
789-804
ISSN
0018-9448 (ISSN-L)
OpenAlex
W2123350510
Cited by
389
References
0
FWCI
41.45
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Capacity of wireless erasure networks. https://app.cochat.ai/papers/10.1109/tit.2005.864424
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Dana, A. F., Gowaikar, R., Palanki, R., Hassibi, B., & Effros, M. (2006). Capacity of wireless erasure networks. IEEE Transactions on Information Theory. https://doi.org/10.1109/tit.2005.864424

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.