Symbol Erasure Correction Capability of Spread Codes

TL;DR

This paper analyzes the erasure correction capabilities of spread codes in network transmission models, comparing their performance and decoding complexity to other codes in row and column erasure channels.

Contribution

It derives the symbol erasure correction capabilities of spread codes for two network channel models and discusses their decoding methods and complexities.

Findings

Spread codes have specific correction capabilities in both models.

Decoding complexity varies between spread codes and alternatives.

Choice of code depends on application and optimization criteria.

Abstract

We consider data transmission over a network where each edge is an erasure channel and where the inner nodes transmit a random linear combination of their incoming information. We distinguish two channel models in this setting, the row and the column erasure channel model. For both models we derive the symbol erasure correction capabilities of spread codes and compare them to other known codes suitable for those models. Furthermore, we explain how to decode these codes in the two channel models and compare their decoding complexities. The results show that, depending on the application and the to-be-optimized aspect, any combination of codes and channel models can be the best choice.