Distributionally Robust Degree Optimization for BATS Codes

TL;DR

This paper introduces a distributionally robust optimization approach for degree distribution in BATS codes, addressing sensitivity issues to empirical rank distributions and ensuring stable, near-optimal network coding performance.

Contribution

It proposes a novel distributionally robust degree optimization framework for BATS codes, improving stability and performance in real-world wireless networks.

Findings

Achieves stable near-optimal rates across varying channel conditions

Reduces rate degradation caused by empirical distribution overestimation

Enhances applicability of BATS codes in practical scenarios

Abstract

Batched sparse (BATS) code is a network coding solution for multi-hop wireless networks with packet loss. Achieving a close-to-optimal rate relies on an optimal degree distribution. Technical challenges arise from the sensitivity of this distribution to the often empirically obtained rank distribution at the destination node. Specifically, if the empirical distribution overestimates the channel, BATS codes experience a significant rate degradation, leading to unstable rates across different runs and hence unpredictable transmission costs. Confronting this unresolved obstacle, we introduce a formulation for distributionally robust optimization in degree optimization. Deploying the resulting degree distribution resolves the instability of empirical rank distributions, ensuring a close-to-optimal rate, and unleashing the potential of applying BATS codes in real-world scenarios.