Adaptive Construction and Decoding of Random Convolutional Network Error-correction Coding

To address unknown topology and delay in practice networks, an adaptive construction and decoding for random convolutional network error correction coding (RCNECC) are considered in this paper. First, we randomly choose local encoding kernel (LEK) for each node over a small field, and the global encoding kernel (GEK) is put in the head of packets. The length of LEK is increased each time until all the sink nodes have the transfer matrix with full rank. Then, the maximum weight of equivalent errors at source node is estimated for the set of possible network errors, and an error correction code able to correct the errors is used before the messages are sent to the network. Further, we extend the Viterbi-like decoding algorithm based on the minimum network-error weight of combination errors to random coding and field Fq. The algorithm can directly decode convolutional codes at the sink node and correct any network error within the capability of RCNECC. Meantime, the distributed decoding of RCNECC has low complexity and decoding delay. Finally, we present an example to show how the construction and decoding algorithm work over F _q .