Title: Distributed Decoding of Convolutional Network Error Correction Codes,
Abstract: A Viterbi-like decoding algorithm is proposed in this paper for generalized
convolutional network error correction coding. Different from classical Viterbi
algorithm, our decoding algorithm is based on minimum error weight rather than
the shortest Hamming distance between received and sent sequences. Network
errors may disperse or neutralize due to network transmission and convolutional
network coding. Therefore, classical decoding algorithm cannot be employed any
more. Source decoding was proposed by multiplying the inverse of network
transmission matrix, where the inverse is hard to compute. Starting from the
Maximum A Posteriori (MAP) decoding criterion, we find that it is equivalent to
the minimum error weight under our model. Inspired by Viterbi algorithm, we
propose a Viterbi-like decoding algorithm based on minimum error weight of
combined error vectors, which can be carried out directly at sink nodes and can
correct any network errors within the capability of convolutional network error
correction codes (CNECC). Under certain situations, the proposed algorithm can
realize the distributed decoding of CNECC.