A position paper proposes replacing token-based representations in foundation models with outcome-driven digital twin representations that explicitly encode physical and semantic structure.
On the Effects of Modeling on the Sim-to-Real Transfer Gap in Twinning the POWDER Platform
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abstract
Digital Twin (DT) technology is expected to play a pivotal role in NextG wireless systems. However, a key challenge remains in the evaluation of data-driven algorithms within DTs, particularly the transfer of learning from simulations to real-world environments. In this work, we investigate the sim-to-real gap in developing a digital twin for the NSF PAWR Platform, POWDER. We first develop a 3D model of the University of Utah campus, incorporating geographical measurements and all rooftop POWDER nodes. We then assess the accuracy of various path loss models used in training modeling and control policies, examining the impact of each model on sim-to-real link performance predictions. Finally, we discuss the lessons learned from model selection and simulation design, offering guidance for the implementation of DT-enabled wireless networks.
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cs.LG 1years
2025 1verdicts
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Position: Foundation Models Need Digital Twin Representations
A position paper proposes replacing token-based representations in foundation models with outcome-driven digital twin representations that explicitly encode physical and semantic structure.