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

arXiv: https://arxiv.org/abs/2512.10874 · HTML: https://mlsanity.com/paper/2512.10874/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 32 references that are in the map.
- First posted on arXiv 11 Dec 2025.

## Builds on (most cited first, 13 of 13)

- [Maximum Flow and Minimum-Cost Flow in Almost-Linear Time](https://mlsanity.com/paper/2203.00671/) (2022)
- [Link Scheduling using Graph Neural Networks](https://mlsanity.com/paper/2109.05536/) (2021)
- [6G for Vehicle-to-Everything (V2X) Communications: Enabling Technologies, Challenges, and Opportunities](https://mlsanity.com/paper/2012.07753/) (2020)
- [Massive Access for 5G and Beyond](https://mlsanity.com/paper/2002.03491/) (2020)
- [The Internet of Battle Things](https://mlsanity.com/paper/1712.08980/) (2017)
- [Wireless Backhaul in 5G and Beyond: Issues, Challenges and Opportunities](https://mlsanity.com/paper/2103.08234/) (2021)
- [Delay-aware Backpressure Routing Using Graph Neural Networks](https://mlsanity.com/paper/2211.10748/) (2022)
- [Learnable Digital Twin for Efficient Wireless Network Evaluation](https://mlsanity.com/paper/2306.06574/) (2023)
- [Congestion-aware Distributed Task Offloading in Wireless Multi-hop Networks Using Graph Neural Networks](https://mlsanity.com/paper/2312.02471/) (2023)
- [GLANCE: Graph-based Learnable Digital Twin for Communication Networks](https://mlsanity.com/paper/2408.09040/) (2024)
- [Biased Backpressure Routing Using Link Features and Graph Neural Networks](https://mlsanity.com/paper/2407.09753/) (2024)
- [Joint Task Offloading and Routing in Wireless Multi-hop Networks Using Biased Backpressure Algorithm](https://mlsanity.com/paper/2412.15385/) (2024)
- [Wi-Fi: Twenty-Five Years and Counting](https://mlsanity.com/paper/2507.09613/) (2025)

## Abstract (arXiv)

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.