The Experts below are selected from a list of 73662 Experts worldwide ranked by ideXlab platform
Tuvi Etzion - One of the best experts on this subject based on the ideXlab platform.
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vector Network Coding based on subspace codes outperforms scalar linear Network Coding
International Symposium on Information Theory, 2016Co-Authors: Tuvi Etzion, Antonia WachterzehAbstract:This paper considers vector Network Coding based on rank-metric codes and subspace codes. Our main result is that vector Network Coding can significantly reduce the required field size compared to scalar linear Network Coding in the same multicast Network. The achieved gap between the field size of scalar and vector Network Coding is in q(h−2)t2/h+o(t) for any q ≥ 2 and any even h ≥ 4, where t denotes the dimension of the vector solution and h the number of messages. If h ≥ 5 is odd, then the achieved gap of the field size between the scalar Network Coding solution and the vector Network Coding solution is q(h−3)t2/(h−1)+o(t). Previously, only a gap of constant size had been shown. This implies also the same gap between the field size in linear and non-linear scalar Network Coding for multicast Networks. The results are obtained by considering several multicast Networks which are variations of the well-known combination Network.
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ISIT - Vector Network Coding based on subspace codes outperforms scalar linear Network Coding
2016 IEEE International Symposium on Information Theory (ISIT), 2016Co-Authors: Tuvi Etzion, Antonia Wachter-zehAbstract:This paper considers vector Network Coding based on rank-metric codes and subspace codes. Our main result is that vector Network Coding can significantly reduce the required field size compared to scalar linear Network Coding in the same multicast Network. The achieved gap between the field size of scalar and vector Network Coding is in q(h−2)t2/h+o(t) for any q ≥ 2 and any even h ≥ 4, where t denotes the dimension of the vector solution and h the number of messages. If h ≥ 5 is odd, then the achieved gap of the field size between the scalar Network Coding solution and the vector Network Coding solution is q(h−3)t2/(h−1)+o(t). Previously, only a gap of constant size had been shown. This implies also the same gap between the field size in linear and non-linear scalar Network Coding for multicast Networks. The results are obtained by considering several multicast Networks which are variations of the well-known combination Network.
Masahito Hayashi - One of the best experts on this subject based on the ideXlab platform.
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Secure physical layer Network Coding versus secure Network Coding
arXiv: Cryptography and Security, 2018Co-Authors: Masahito HayashiAbstract:Secure Network Coding realizes the secrecy of the message when the message is transmitted via noiseless Network and a part of edges or a part of intermediate nodes are eavesdropped. In this framework, if the channels of the Network has noise, we apply the error correction to noisy channel before applying the secure Network Coding. In contrast, secure physical layer Network Coding is a method to securely transmit a message by a combination of Coding operation on nodes when the Network is given as a set of noisy channels. In this paper, we give several examples of Network, in which, secure physical layer Network Coding has advantage over secure Network Coding.
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ITW - Secure physical layer Network Coding versus secure Network Coding
2018 IEEE Information Theory Workshop (ITW), 2018Co-Authors: Masahito HayashiAbstract:Secure Network Coding realizes the secrecy of the message when the message is transmitted via noiseless Network and a part of edges or a part of intermediate nodes are eavesdropped. In this framework, if the channels of the Network has noise, we apply the error correction to noisy channel before applying the secure Network Coding. In contrast, secure physical layer Network Coding is a method to securely transmit a message by a combination of Coding operation on nodes when the Network is given as a set of noisy channels. In this paper, we give several examples of Network, in which, secure physical layer Network Coding realizes a performance that cannot be realized by secure Network Coding.
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secure multiplex Network Coding
arXiv: Information Theory, 2011Co-Authors: Ryutaroh Matsumoto, Masahito HayashiAbstract:In the secure Network Coding for multicasting, there is loss of information rate due to inclusion of random bits at the source node. We show a method to eliminate that loss of information rate by using multiple statistically independent messages to be kept secret from an eavesdropper. The proposed scheme is an adaptation of Yamamoto et al.'s secure multiplex Coding to the secure Network Coding.
Antonia Wachterzeh - One of the best experts on this subject based on the ideXlab platform.
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vector Network Coding based on subspace codes outperforms scalar linear Network Coding
International Symposium on Information Theory, 2016Co-Authors: Tuvi Etzion, Antonia WachterzehAbstract:This paper considers vector Network Coding based on rank-metric codes and subspace codes. Our main result is that vector Network Coding can significantly reduce the required field size compared to scalar linear Network Coding in the same multicast Network. The achieved gap between the field size of scalar and vector Network Coding is in q(h−2)t2/h+o(t) for any q ≥ 2 and any even h ≥ 4, where t denotes the dimension of the vector solution and h the number of messages. If h ≥ 5 is odd, then the achieved gap of the field size between the scalar Network Coding solution and the vector Network Coding solution is q(h−3)t2/(h−1)+o(t). Previously, only a gap of constant size had been shown. This implies also the same gap between the field size in linear and non-linear scalar Network Coding for multicast Networks. The results are obtained by considering several multicast Networks which are variations of the well-known combination Network.
Antonia Wachter-zeh - One of the best experts on this subject based on the ideXlab platform.
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ISIT - Vector Network Coding based on subspace codes outperforms scalar linear Network Coding
2016 IEEE International Symposium on Information Theory (ISIT), 2016Co-Authors: Tuvi Etzion, Antonia Wachter-zehAbstract:This paper considers vector Network Coding based on rank-metric codes and subspace codes. Our main result is that vector Network Coding can significantly reduce the required field size compared to scalar linear Network Coding in the same multicast Network. The achieved gap between the field size of scalar and vector Network Coding is in q(h−2)t2/h+o(t) for any q ≥ 2 and any even h ≥ 4, where t denotes the dimension of the vector solution and h the number of messages. If h ≥ 5 is odd, then the achieved gap of the field size between the scalar Network Coding solution and the vector Network Coding solution is q(h−3)t2/(h−1)+o(t). Previously, only a gap of constant size had been shown. This implies also the same gap between the field size in linear and non-linear scalar Network Coding for multicast Networks. The results are obtained by considering several multicast Networks which are variations of the well-known combination Network.
Raymond W. Yeung - One of the best experts on this subject based on the ideXlab platform.
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Network Coding theory: An introduction
Frontiers of Electrical and Electronic Engineering in China, 2010Co-Authors: Raymond W. YeungAbstract:For a long time, store-and-forward had been the transport mode in Network communications. In other words, information had been regarded as a commodity that only needs to be routed through the Network, possibly with replication at the intermediate nodes. In the late 1990’s, a new concept called Network Coding fundamentally changed the way a Network can be operated. Under the paradigm of Network Coding, information can be processed within the Network for the purpose of transmission. It was demonstrated that compared with store-and-forward, the Network throughput can generally be increased by employing Network Coding. Since then, Network Coding has made significant impact on different branches of information science. The impact of Network Coding has gone as far as mathematics, physics, and biology. This expository work aims to be an introduction to this fast-growing subject with a detailed discussion of the basic theoretical results.
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Variable-Rate Linear Network Coding
IEEE Transactions on Information Theory, 2010Co-Authors: Silas L. Fong, Raymond W. YeungAbstract:We introduce variable-rate linear Network Coding for single-source finite acyclic Network. In this problem, the source of a Network transmits messages at different rates in different time sessions and every nonsource node in the Network decodes the messages if possible. We propose two efficient algorithms for implementing variable-rate linear Network Coding under different circumstances.
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Avalanche: A Network Coding Analysis
Communications in Information and Systems, 2007Co-Authors: Raymond W. YeungAbstract:In this paper, we study the application of random Network Coding in peer-to-peer (P2P) Networks. The system we analyze is based on a prototype called Avalanche proposed in [8] for large scale content distribution on such Networks. We present the necessary techniques for analyzing the system and show that random Network Coding provides the system with both maximum bandwidth efficiency and robustness. We also point out that the model for random Network Coding in P2P Networks is very different from the one that has been studied extensively in the literature.
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Network Coding theory single sources
Foundations and Trends in Communications and Information Theory, 2005Co-Authors: Raymond W. Yeung, Shuo-yen Robert Li, Zhen ZhangAbstract:Store-and-forward had been the predominant technique for transmitting information through a Network until its optimality was refuted by Network Coding theory. Network Coding offers a new paradigm for Network communications and has generated abundant research interest in information and Coding theory, Networking, switching, wireless communications, cryptography, computer science, operations research, and matrix theory.
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secure Network Coding
International Symposium on Information Theory, 2002Co-Authors: Ning Cai, Raymond W. YeungAbstract:Recent work on Network Coding renders a new view on multicasting in a Network. In the paradigm of Network Coding, the nodes in a Network are allowed to encode the information received from the input links. The usual function of switching at a node is a special case of Network Coding. The advantage of Network Coding is that the full capacity of the Network can be utilized. In this paper, we propose a new model which incorporates Network Coding and information security. Specifically, a collection of subsets of links is given, and a wiretapper is allowed to access any one (but not more than one) of these subsets without being able to obtain any information about the message transmitted. Our model includes secret sharing as a special case. We present a construction of secure linear Network codes provided a certain graph-theoretic sufficient condition is satisfied.
