NS3Learn is a closed‑form model that captures realistic 5G NR sidelink Mode‑2 reception losses—such as half‑duplex loss, scheduling collisions, receiver capture, and decoding—by fitting 10.5 million labeled outcomes from ns‑3 5G‑LENA traces. The model achieves a mean absolute deviation of 0.06 in per‑instant delivery compared to ns‑3, outperforming alternative models, and its parameters transfer with minimal error to new intersections. Using NS3Learn in traffic‑network simulations reverses traffic speed trends and more than doubles predicted hard‑braking events, demonstrating its impact on safety assessments.
By Rasheed Bello, Arthur Mukwaya, Gurcan Comert, Varghese Vaidyan, Vijay Bendigeri, Anthony Dontoh, Jagruti Sahoo, Judith Mwakalonge
arXiv:2603. 06607v2 Announce Type: replace-cross Abstract: Radio resource allocation (RRA) is a critical function in cellular vehicle-to-everything (C-V2X) networks, where vehicles must share limited wireless resources to support safety-critical communications.
By Siyuan Wang, Lei Lei, Pranav Maheshwari, Sam Bellefeuille, Kan Zheng
arXiv:2606. 02287v1 Announce Type: cross Abstract: Urban trajectory generation is a fundamental task for transportation simulation, urban planning, and mobility analytics.
By Shibo Zhu, Xiaodan Shi, Dayin Chen, Yuntian Chen, Haoran Zhang, Tianhao Wu, Jinyue Yan
arXiv:2607. 16930v1 Announce Type: cross Abstract: Throughput prediction is foundational for artificial intelligence-driven 6G resource orchestration.
By Muhammad Kabeer, Rosdiadee Nordin, Nadiva Nuriftitah, Sian Lun Lau
arXiv:2607. 20554v1 Announce Type: new Abstract: Reliable and low-latency NR-V2X communications are essential for smart mobility in dense urban environments.
By Giambattista Amati, Federica Mangiatordi, Simone Angelini, Emiliano Pallotti, Pierpaolo Salvo
The paper introduces NOSTRAdAMUS, a predictive link‑adaptation framework for 5G NR that forecasts retransmissions in the next radio frame using recent HARQ history and adjusts the Modulation and Coding Scheme accordingly. Gradient Boosting models achieve 82.9% overall accuracy, with high‑confidence predictions correct 94.2% of the time and a 5.5 µs inference latency. Evaluated OTA on the X5G testbed and various channel emulators, the approach boosts goodput by up to 71.5% and cuts retransmissions by up to 71.8% without retraining across diverse scenarios.
By Tamerlan Aghayev, Maxime Elkael, Michele Polese, Reshma Prasad, Salvatore D'Oro, Yunseong Lee, Koichiro Furueda, Tommaso Melodia
arXiv:2606. 09539v1 Announce Type: new Abstract: Spatio-temporal graph neural networks (STGNNs) have become the dominant approach for traffic prediction, yet their computational requirements pose challenges for practical deployment in intelligent transportation systems (ITS).
By Soban Nasir Lone, Mohamed Abouelela, Taeyoung Yu, Jiwon Kim, Constantinos Antoniou
arXiv:2608.30512v1 Announce Type: cross
Abstract: Large-scale industrial robot fleets share constrained physical infrastructure, making vehicle travel times dependent on safety separation, intersecti...
By Cheng Gu, Qiusheng Zhao, Anbang Liu, Shaochong Lin, Max Z. J. Shen
arXiv:2608. 14583v1 Announce Type: cross Abstract: Vehicular Ad Hoc Networks (VANETs) are a key component of intelligent transportation systems, enabling real-time communication between vehicles.
By Henry Agyapong
The paper introduces MiLAAP, an attention‑based framework that predicts channel occupancy and node motion in channel‑hopping communication systems without exchanging state information. By leveraging self‑attention and multi‑head attention, each node passively observes local channel activity to forecast interference patterns and mobility, achieving near‑perfect prediction accuracy across varied mobility scenarios and demonstrating zero‑shot generalizability to new channel‑sequence periods.
By Yung-Fu Chen, Anish Arora
arXiv:2607. 14176v1 Announce Type: new Abstract: Reliable, low-latency uplink connectivity is a key requirement for C-V2X networks in dense urban environments, where fast channel variations and blockages often degrade direct vehicle-to-infrastructure links.
By Giambattista Amati, Federica Mangiatordi, Emiliano Pallotti, Simone Angelini, Pierpaolo Salvo, Paola Vocca
arXiv:2603.16497v3 Announce Type: replace-cross
Abstract: Time series foundation models (TSFMs) require diverse, real-world datasets to adapt across varying domains and temporal frequencies. However,...
By Subina Khanal, Seshu Tirupathi, Merim Dzaferagic, Marco Ruffini, Torben Bach Pedersen