Convolutional Codes in Rank Metric with Application to Random Network Coding
arXiv:1404.7251 · doi:10.1109/TIT.2015.2424930
Abstract
Random network coding recently attracts attention as a technique to disseminate information in a network. This paper considers a non-coherent multi-shot network, where the unknown and time-variant network is used several times. In order to create dependencies between the different shots, particular convolutional codes in rank metric are used. These codes are so-called (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 maximum rank distance codes are presented. Second, an efficient error-erasure decoding algorithm for these codes is presented. Its guaranteed decoding radius is derived and its complexity is bounded. Finally, it is shown how to apply these codes for error correction in random linear and affine network coding.
presented in part at Netcod 2012, submitted to IEEE Transactions on Information Theory
References in corpus (1)
Cited by in corpus (8)
- Reliable and Secure Multishot Network Coding using Linearized Reed-Solomon Codes
- Locally Repairable Convolutional Codes with Sliding Window Repair
- Error-Erasure Decoding of Linearized Reed-Solomon Codes in the Sum-Rank Metric
- Distributed Decoding of Convolutional Network Error Correction Codes
- Error Correction for Differential Linear Network Coding in Slowly-Varying Networks
- Fast Decoding of Interleaved Linearized Reed-Solomon Codes and Variants
- On the Success Probability of Decoding (Partial) Unit Memory Codes
- Convolutional Codes with Maximum Column Sum Rank for Network Streaming