  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.
