Density optimization decoding method based on sparse network coding

Abstract

The invention provides a density optimization decoding method based on sparse network coding in a wireless network. The method is used for solving the problem that decoding transmission overhead is increased due to high-sparseness data packets in a relay cooperation network. The method comprises the following steps that: a relay node calculates a linear dependency relationship value according to a coding packet transmitted by an information source node; the relay node judges whether the linear dependency relationship value and the decoding critical value exceed the linear dependency extreme value range or not to select a decoding mode, and if the linear dependency relationship value and the decoding critical value exceed the linear dependency extreme value range, the relay node sends a coding packet to the sink node in a storage-forwarding mode to complete decoding; otherwise, the relay node updates the density value and then enters the next round of data packet transmission or directly decodes the coding packet part. According to the method, the relay node is allowed to cache the data packet, different coding density values are selected according to the decoding state and partial decoding is carried out, the coding and decoding opportunity can be increased, the decoding probability can be improved, the transmission overhead can be reduced, the decoding gain can be maximized, and the transmission effectiveness of the data packet can be effectively improved.

Description

technical field [0001] The invention belongs to the technical field of density adjustment in wireless networks, in particular to a density-optimized decoding method based on sparse network coding. Background technique [0002] With the development of the information revolution, the human communication system has developed rapidly, from the initial postal communication to wired communication, and then to the final wireless communication. The requirements for wireless communication services are also constantly increasing, such as how to increase the transmission bandwidth, how to meet the fast transmission of high-definition video streams, etc. Therefore, higher throughput and higher data transmission efficiency have become important issues in wireless communication. In the traditional data transmission method, the nodes can only store and forward the received data packets sequentially, resulting in more transmission times, lower transmission efficiency, and larger network ove...

AI Technical Summary

Problems solved by technology

[0005] In view of this, this application mainly aims at the problems in the traditional sparse network coding that the decoding probability of the sink node decreases due to the increase in the probability of receiving a linear packet in the later stage of transmission, and the slow growth of the matrix rank leads to an increase in transmission overhead. The density adjustment method of network coding uses the additional transmission overhead required by the linear dependence on the total number as the density adjustment range to improve transmission efficiency

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