Quality follows upgrading
Tag: cs.NI
arXiv:2603.22343v1 Announce Type: new Abstract: Photovoltaic (PV) power forecasting in edge-enabled grids requires balancing forecasting accuracy, robustness under weather-driven distribution shifts, and strict latency constraints. …
arXiv:2603.22465v1 Announce Type: new Abstract: Federated Learning (FL) is constrained by the communication and energy limitations of decentralized edge devices. While gradient sparsification via Top-K …
arXiv:2603.18197v1 Announce Type: new Abstract: Recent studies reveal gaps in delegating critical tasks to agentic AI that accesses websites on the user's behalf, primarily due …
arXiv:2603.11475v1 Announce Type: new Abstract: Time series analysis is critical for emerging net- work intelligent control and management functions. However, existing statistical-based and shallow machine …
arXiv:2603.10564v1 Announce Type: new Abstract: The integration of Generative AI models into AI-native network systems offers a transformative path toward achieving autonomous and adaptive control. …
arXiv:2603.08717v1 Announce Type: new Abstract: AI-enabled Radio Access Networks (AI-RANs) are expected to serve heterogeneous users with time-varying learning tasks over shared edge resources. Ensuring …
arXiv:2603.09082v1 Announce Type: new Abstract: To support latency-sensitive Internet of Vehicles (IoV) applications amidst dynamic environments and intermittent links, this paper proposes a Reconfigurable Intelligent …
arXiv:2603.04898v1 Announce Type: new Abstract: This demonstration presents U-Parking, a distributed Ultra-Wideband (UWB)-assisted autonomous parking system. By integrating Large Language Models (LLMs)-assisted planning with robust …
arXiv:2602.16762v1 Announce Type: new Abstract: Modern machine learning-based wireless localization using Wi-Fi signals continues to face significant challenges in achieving groundbreaking performance across diverse environments. …
arXiv:2602.15510v1 Announce Type: new Abstract: Federated Learning (FL) enables distributed training across multiple clients without centralized data sharing, while Graph Neural Networks (GNNs) model relational …
