  The problem of coding for networks experiencing worst-case symbol errors is
considered. We argue that this is a reasonable model for highly dynamic
wireless network transmissions. We demonstrate that in this setup prior network
error-correcting schemes can be arbitrarily far from achieving the optimal
network throughput. A new transform metric for errors under the considered
model is proposed. Using this metric, we replicate many of the classical
results from coding theory. Specifically, we prove new Hamming-type,
Plotkin-type, and Elias-Bassalygo-type upper bounds on the network capacity. A
commensurate lower bound is shown based on Gilbert-Varshamov-type codes for
error-correction. The GV codes used to attain the lower bound can be
non-coherent, that is, they do not require prior knowledge of the network
topology. We also propose a computationally-efficient concatenation scheme. The
rate achieved by our concatenated codes is characterized by a Zyablov-type
lower bound. We provide a generalized minimum-distance decoding algorithm which
decodes up to half the minimum distance of the concatenated codes. The
end-to-end nature of our design enables our codes to be overlaid on the
classical distributed random linear network codes [1]. Furthermore, the
potentially intensive computation at internal nodes for the link-by-link
error-correction is un-necessary based on our design.
