Generation grouped cross-random linear network coding method

A technology of network coding and random linearity, applied in the field of communication, can solve the problems of unable to solve the original data successfully alone, computational complexity and space complexity, etc., to reduce time complexity and space complexity, reduce impact, improve The effect of decoding speed

Inactive Publication Date: 2013-01-23
XIDIAN UNIV
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  • Abstract
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Problems solved by technology

However, its disadvantage is that it cannot successfully solve the original data of a certain generation alone, and its computational complexity and space complexity are too large

Method used

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  • Generation grouped cross-random linear network coding method
  • Generation grouped cross-random linear network coding method
  • Generation grouped cross-random linear network coding method

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Embodiment Construction

[0024] The present invention will be described in further detail below with reference to the accompanying drawings.

[0025] One, the mathematical theory used in the present invention and explanation of technical terms

[0026] 1. The full rank theorem of sparse matrices in finite fields:

[0027] Let M=(m in ) n×n is an n×n-dimensional random matrix on the finite field GF(q), and the element m in the matrix ij obey independent uniform distributions with the following probabilities:

[0028] Pr ( m ij = m ) = 1 - p m = 0 p / ( q - ...

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Abstract

The invention discloses a generation grouped cross-random linear network coding method which comprises the following steps of: sequentially differentiating the generations of original data by an information source, sequentially grouping in each generation and forming a generation set by a generation and the next generation; coding in each generation set, wherein the coding process of each generation sets is independent, and confirming the information package structures of the generation sets by the information source; confirming local sparse coding coefficient vectors according to the number of coding-participating information packages by a node, then, generating a new coding package according to the local sparse coding coefficient vectors and the coding-participating information packagesand sending the new coding package to downstream nodes; carrying out the linear correlation detection of the received new coding package by a node, using the new coding package for coding or decodingif the new coding package passes the detection or else discarding or retransmitting; and adopting a Gaussian column principal component elimination method to decode the original data by a destinationoption node after receiving enough coding packages passing the linear correlation detection. The invention has the advantages of lowering the network coding time and the space complexity and weakening the necessary conditions for correct coding, thereby being suitable for information transmission in information sharing.

Description

technical field [0001] The invention belongs to the technical field of communication and relates to network coding, in particular to a random linear network coding method used for information transmission in the process of information sharing. Background technique [0002] In 2000, the network coding theory proposed by Ashlswede et al. for the first time subverted the store-and-forward routing mode in the traditional communication network, which allows the intermediate nodes of the network communication to process the information it receives. The application of network coding has brought many advantages to the existing network, including increasing network throughput, improving network reliability, reducing communication energy consumption, improving bandwidth utilization, ensuring information security, improving robustness and fault tolerance sex etc. Most of the existing file sharing processes use the HTTP protocol or similar protocols to achieve data transmission, and th...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04L1/00
Inventor 李学俊李晖申肖肖王博洋
Owner XIDIAN UNIV
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