Convolutional Codes in Rank Metric With Application to Random Network Coding
PROJECT TITLE :
Random network coding recently attracts attention as a technique to disseminate info in an exceedingly network. This paper considers a noncoherent multishot network, where the unknown and time-variant network is used several times. In order to create dependence between the various shots, explicit convolutional codes in rank metric are used. These codes are thus-known as (partial) unit memory ((P)UM) codes, i.e., convolutional codes with memory one. First, distance measures for convolutional codes in rank metric are shown and two constructions of (P)UM codes in rank metric based on the generator matrices of most rank distance codes are presented. Second, an economical error-erasure decoding algorithm for these codes is presented. Its guaranteed decoding radius comes and its complexity is bounded. Finally, it’s shown how to apply these codes for error correction in random linear and affine network coding.
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