A Matroid Framework for Noncoherent Random Network Communications

TL;DR

This paper introduces a matroid-based framework for noncoherent network coding, proposing a new protocol called RANC that improves data rate and throughput over RLNC by utilizing affine combinations of packets.

Contribution

It models network communications as matroid flats, introduces RANC protocol, and develops nearly optimal codes with efficient decoding for improved network performance.

Findings

RANC outperforms RLNC in data rate and throughput.

The framework unifies error control models for RLNC and SAF.

Proposed codes achieve near-optimal error correction with low complexity.

Abstract

Models for noncoherent error control in random linear network coding (RLNC) and store and forward (SAF) have been recently proposed. In this paper, we model different types of random network communications as the transmission of flats of matroids. This novel framework encompasses RLNC and SAF and allows us to introduce a novel protocol, referred to as random affine network coding (RANC), based on affine combinations of packets. Although the models previously proposed for RLNC and SAF only consider error control, using our framework, we first evaluate and compare the performance of different network protocols in the error-free case. We define and determine the rate, average delay, and throughput of such protocols, and we also investigate the possibilities of partial decoding before the entire message is received. We thus show that RANC outperforms RLNC in terms of data rate and throughput thanks to a more efficient encoding of messages into packets. Second, we model the possible alterations of a message by the network as an operator channel, which generalizes the channels proposed for RLNC and SAF. Error control is thus reduced to a coding-theoretic problem on flats of a matroid, where two distinct metrics can be used for error correction. We study the maximum cardinality of codes on flats in general, and codes for error correction in RANC in particular. We finally design a class of nearly optimal codes for RANC based on rank metric codes for which we propose a low-complexity decoding algorithm. The gain of RANC over RLNC is thus preserved with no additional cost in terms of complexity.