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From the 1 of 29 linked papers with an AI index.

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20242026
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cs.LG2026

Personalized Federated Distillation Assisted Vehicle Edge Caching Strategy

Xun Li, Qiong Wu, Pingyi Fan +3

Vehicle edge caching is a promising technology that can significantly reduce the latency for vehicle users (VUs) to access content by pre-caching user-interested content at edge no…

cs.LG2026

U-Parking: Distributed UWB-Assisted Autonomous Parking System with Robust Localization and Intelligent Planning

Yiang Wu, Qiong Wu, Pingyi Fan +4

This demonstration presents U-Parking, a distributed Ultra-Wideband (UWB)-assisted autonomous parking system. By integrating Large Language Models (LLMs)-assisted planning with rob…

cs.LG2025

Semantic-Aware Cooperative Communication and Computation Framework in Vehicular Networks

Jingbo Zhang, Maoxin Ji, Qiong Wu +3

Semantic Communication (SC) combined with Vehicular edge computing (VEC) provides an efficient edge task processing paradigm for Internet of Vehicles (IoV). Focusing on highway sce…

cs.LG2025

Velocity and Density-Aware RRI Analysis and Optimization for AoI Minimization in IoV SPS

Maoxin Ji, Tong Wang, Qiong Wu +3

Addressing the problem of Age of Information (AoI) deterioration caused by packet collisions and vehicle speed-related channel uncertainties in Semi-Persistent Scheduling (SPS) for…

cs.LG2025

DRL-Based Federated Self-Supervised Learning for Task Offloading and Resource Allocation in ISAC-Enabled Vehicle Edge Computing

Xueying Gu, Qiong Wu, Pingyi Fan +3

Intelligent Transportation Systems (ITS) leverage Integrated Sensing and Communications (ISAC) to enhance data exchange between vehicles and infrastructure in the Internet of Vehic…

cs.LG2025

Resource Allocation for Twin Maintenance and Computing Task Processing in Digital Twin Vehicular Edge Computing Network

Yu Xie, Qiong Wu, Pingyi Fan +4

As a promising technology, vehicular edge computing (VEC) can provide computing and caching services by deploying VEC servers near vehicles. However, VEC networks still face challe…