On the Effects of Modeling on the Sim-to-Real Transfer Gap in Twinning the POWDER Platform
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
mTSBench: Benchmarking Multivariate Time Series Anomaly Detection and Model Selection at Scale
Leveraging Predictive Equivalence in Decision Trees
The use of cross validation in the analysis of designed experiments
Router-R1: Teaching LLMs Multi-Round Routing and Aggregation via Reinforcement Learning
Tuning the Right Foundation Models is What you Need for Partial Label Learning
Behavioral Augmentation of UML Class Diagrams: An Empirical Study of Large Language Models for Method Generation
토론
이 논문과 연결된 포럼 글이 아직 없습니다.