A Markov Chain Model for the Decoding Probability of Sparse Network Coding

TL;DR

This paper introduces an analytical Markov chain model to accurately predict the decoding probability of Sparse Network Coding, enabling better parameter tuning and performance assessment compared to existing bounds.

Contribution

It presents a novel Markov chain model for SNC decoding probability, validated with simulations, improving accuracy over previous bounds and aiding adaptive coding strategies.

Findings

Model achieves negligible error compared to simulations

Provides a more precise performance assessment of SNC

Enables optimized density selection in TSNC techniques

Abstract

Random Linear Network Coding (RLNC) has been proved to offer an efficient communication scheme, leveraging an interesting robustness against packet losses. However, it suffers from a high computational complexity and some novel approaches, which follow the same idea, have been recently proposed. One of such solutions is Tunable Sparse Network Coding (TSNC), where only few packets are combined in each transmissions. The amount of data packets to be combined in each transmissions can be set from a density parameter/distribution, which could be eventually adapted. In this work we present an analytical model that captures the performance of SNC on an accurate way. We exploit an absorbing Markov process where the states are defined by the number of useful packets received by the decoder, i.e the decoding matrix rank, and the number of non-zero columns at such matrix. The model is validated by means of a thorough simulation campaign, and the difference between model and simulation is negligible. A mean square error less than $4 \cdot 10^{-4}$ in the worst cases. We also include in the comparison some of more general bounds that have been recently used, showing that their accuracy is rather poor. The proposed model would enable a more precise assessment of the behavior of sparse network coding techniques. The last results show that the proposed analytical model can be exploited by the TSNC techniques in order to select by the encoder the best density as the transmission evolves.