Error Correction for Differential Linear Network Coding in Slowly-Varying Networks

TL;DR

This paper extends differential linear network coding (DLNC) to account for slow network changes, proposing a new channel model and a matched coding scheme to improve robustness in dynamic network environments.

Contribution

It introduces an extended DLNC channel model for slowly-varying networks and proposes a rank-metric convolutional coding scheme tailored to this scenario.

Findings

Extended DLNC model incorporating slow network variations

Proposed rank-metric convolutional coding scheme for improved error correction

Enhanced robustness of DLNC in dynamic network conditions

Abstract

Differential linear network coding (DLNC) is a precoding scheme for information transmission over random linear networks. By using differential encoding and decoding, the conventional approach of lifting, required for inherent channel sounding, can be omitted and in turn higher transmission rates are supported. However, the scheme is sensitive to variations in the network topology. In this paper, we derive an extended DLNC channel model which includes slow network changes. Based on this, we propose and analyze a suitable channel coding scheme matched to the situation at hand using rank-metric convolutional codes.