Fly-PRAC: Packet Recovery for Random Linear Network Coding

TL;DR

Fly-PRAC introduces a novel packet recovery method for network coding that estimates corrupted packets algebraically at intermediate nodes, significantly improving efficiency and reducing transmissions in noisy networks.

Contribution

Fly-PRAC is the first scheme to recover coded packets at intermediate nodes without decoding, outperforming previous methods like S-PRAC especially in noisy conditions.

Findings

Fly-PRAC doubles the performance of S-PRAC at a bit error rate of 10^-4 for 900B payload.

In two-hop networks, Fly-PRAC reduces transmissions by 16% by enabling recovery at intermediate nodes.

In Sparse Network Coding, Fly-PRAC decreases decoding delay by 31% on average.

Abstract

Network Coding (NC) is a compelling solution for increasing network efficiency. However, it discards corrupted packets and cannot achieve optimal performance in noisy communications. Since most of the information in corrupted packets is error-free, discarding them is not the best strategy. Several packet recovery techniques such as PRAC and S-PRAC were proposed to exploit corrupted packets. Yet, they are slow and only practical when the packet size is small and communication channels are not very noisy. We propose a packet recovery scheme called Fly-PRAC to address these issues. Fly-PRAC exploits algebraic relations between a group of coded packets to estimate their corrupted parts and recovers them. Unlike previous schemes, Fly-PRAC can recover coded packets at the intermediate node without decoding them. We have compared Fly-PRAC against S-PRAC. Results show when the bit error rate ({\epsilon}) is 10^-4, Fly-PRAC outperforms S-PRAC by two folds for a payload of 900B. In two-hop communication with {\epsilon} = 10^-4 and a payload size of 500B, by enabling the recovery in the intermediate node, Fly-PRAC reduces transmissions by 16%. In a Sparse Network Coding (SNC) scenario, with two non-zero elements in the coefficient vectors and a payload of 800B, there is a reduction by 31% on average for decoding delay.