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

2025-12-11

AI Summary

In wireless multihop networks, link capacity exhibits nonlinear, environment-dependent behavior due to stochastic channel contention, rendering classical minimum-cost flow approaches ineffective. To address this, we propose a differentiable Network Digital Twin (NDT) framework: (i) it integrates the weighted Luby algorithm into conflict graph modeling to accurately capture distributed random access; (ii) it derives an analytical expression for link duty cycle and resolves the cyclic dependency among duty cycle, capacity, and contention probability via implicit function iteration; and (iii) it enables end-to-end differentiable link scheduling optimization. Experiments demonstrate that NDT achieves low prediction error for duty cycles and congestion patterns, accelerates computation by 5,000× over packet-level simulation, and significantly reduces network congestion and RF resource utilization.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Distributed Search

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Web Mining and Content Analysis: Content-based information diffusion

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.

Problem

• Modeling wireless link capacity under contention
• Predicting link duty cycles in multi-hop networks
• Optimizing scheduling via differentiable digital twin

Innovation

• Analytical network digital twin predicts link duty cycles
• Generalizes contention as Maximal Independent Set with weighted Luby's algorithm
• Enables gradient descent optimization for reduced congestion and footprint