A Differentiable Digital Twin of Distributed Link Scheduling for Contention-Aware Networking

TL;DR

This paper introduces a differentiable digital twin model for wireless networks that predicts link duty cycles and congestion patterns efficiently, enabling optimized link scheduling through gradient-based methods.

Contribution

It develops an analytical network digital twin that models wireless contention and duty cycles, providing a fast and accurate alternative to packet-level simulation.

Findings

Accurately predicts link duty cycles and congestion patterns

Achieves up to 5000x speedup over packet-level simulation

Enables gradient-based optimization of link scheduling

Abstract

Many routing and flow optimization problems in wired networks can be solved efficiently using minimum cost flow formulations. However, this approach does not extend to wireless multi-hop networks, where the assumptions of fixed link capacity and linear cost structure collapse due to contention for shared spectrum resources. The key challenge is that the long-term capacity of a wireless link becomes a non-linear function of its network context, including network topology, link quality, and the traffic assigned to neighboring links. In this work, we pursue a new direction of modeling wireless network under randomized medium access control by developing an analytical network digital twin (NDT) that predicts link duty cycles from network context. We generalize randomized contention as finding a Maximal Independent Set (MIS) on the conflict graph using weighted Luby's algorithm, derive an analytical model of link duty cycles, and introduce an iterative procedure that resolves the circular dependency among duty cycle, link capacity, and contention probability. Our numerical experiments show that the proposed NDT accurately predicts link duty cycles and congestion patterns with up to a 5000x speedup over packet-level simulation, and enables us to optimize link scheduling using gradient descent for reduced congestion and radio footprint.