Publication

Code Construction for Multiple Sources Network Coding

Christina Fragouli, Suhas Diggavi
2009
Conference Papers

Abstract

In several wireless applications multiple sources transmit information to one or more receivers, many times over unknown topologies. This is especially so in mobile networks where learning the topology may have prohibitive complexity. Network coding techniques allow to achieve the min-cut capacity even when the topology is unknown. Our contribution in this paper is to develop algebraic code constructions for multiple sources network coding.

Official source

https://infoscience.epfl.ch/entities/publication/47b1cf01-6b97-4525-b11c-a6d9bcac26d3

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Christina Fragouli, Suhas Diggavi
2009
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/47b1cf01-6b97-4525-b11c-a6d9bcac26d3

About this result

Related concepts (19)

Coding theory

Coding theory is the study of the properties of codes and their respective fitness for specific applications. Codes are used for data compression, cryptography, error detection and correction, data transmission and data storage. Codes are studied by various scientific disciplines—such as information theory, electrical engineering, mathematics, linguistics, and computer science—for the purpose of designing efficient and reliable data transmission methods.

Linear network coding

In computer networking, linear network coding is a program in which intermediate nodes transmit data from source nodes to sink nodes by means of linear combinations. Linear network coding may be used to improve a network's throughput, efficiency, and scalability, as well as reducing attacks and eavesdropping. The nodes of a network take several packets and combine for transmission. This process may be used to attain the maximum possible information flow in a network.

Convolutional code

In telecommunication, a convolutional code is a type of error-correcting code that generates parity symbols via the sliding application of a boolean polynomial function to a data stream. The sliding application represents the 'convolution' of the encoder over the data, which gives rise to the term 'convolutional coding'. The sliding nature of the convolutional codes facilitates trellis decoding using a time-invariant trellis. Time invariant trellis decoding allows convolutional codes to be maximum-likelihood soft-decision decoded with reasonable complexity.

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