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
The paper presents a large‑scale, 3GPP TR 38.901‑compliant dataset for RIS‑aided millimeter‑wave B5G networks, covering 20 deployment variants with diverse user densities, fading, and blockage conditions. Each sample includes oracle RIS phase configurations from a brute‑force search, along with full CSI, per‑link channel decomposition, optimal phase matrices, and CQI labels, enabling a wide range of machine‑learning tasks. The authors also introduce a novel CSI‑to‑CQI mapping as a benchmark for scalable link‑quality prediction and evaluate it against state‑of‑the‑art models under various conditions.
By Pujitha Mamillapalli, Pankaj Singh Rathour, Abhinav Kumar
arXiv:2607. 09798v1 Announce Type: cross Abstract: Sixth-generation (6G) networks are moving toward AI-native operation, where learning modules are embedded across the radio access network (RAN), edge, and core.
By Sheikh Salman Hassan, Irshad A. Meer, Almoatssimbillah Saifaldawla, Yan Kyaw Tun, Mustafa Ozger, Madyan Alsenwi, Nguyen Van Huynh, Woong-Hee Lee, Cedomir Stefanovic, Mathini Sellathurai, Henk Wymeersch, Tharmalingam Ratnarajah
arXiv:2608. 14694v1 Announce Type: new Abstract: Foundation models are emerging as a transformative paradigm for AI-native sixth-generation (6G) wireless networks by enabling scalable, transferable, and data-efficient intelligence across diverse communication tasks.
By Naveed Khan, Besan Al Sbeihi, Maryam Alshehhi, Nasir Saeed
arXiv:2607. 16066v1 Announce Type: cross Abstract: Agentic Artificial Intelligence (AI), enabled by Large Language Models, marks a shift from rule-based automation toward autonomous, goal-driven control of Next-Generation Networks (NGNs).
By Mazene Ameur, Abdelkader Mekrache, Bouziane Brik, Adlen Ksentini
arXiv:2606. 19821v1 Announce Type: new Abstract: Key Performance Measurement (KPM) forecasting is essential for proactive network management of 5G and next-generation telecom networks.
By Geon Kim, Dara Ron, Sukhdeep Singh, Suyog Moogi, Pranshav Gajjar, V V N K Someswara Rao Koduri, Een Kee Hong, Vijay K. Shah
arXiv:2511. 08851v5 Announce Type: replace-cross Abstract: This paper presents a measurement-driven study of early warning for reliability breakdown events in 5G non-standalone (NSA) railway networks.
By Po-Heng Chou, Da-Chih Lin, Hung-Yu Wei, Walid Saad, Yu Tsao
arXiv:2607. 15469v1 Announce Type: cross Abstract: Federated Learning (FL) over 5G cellular networks protects raw data but remains vulnerable to side-channel leakage.
By Md Nahid Hasan Shuvo, Mahmudul Hassan Ashik, Moinul Hossain
The paper introduces DRIFT, a lightweight framework for joint channel estimation and prediction in low Earth orbit non-terrestrial networks, aiming to reduce pilot overhead by using data-driven processing after the initial slot. DRIFT refines data-aided channel estimates and forecasts future channel responses with low computational cost, offering two variants based on convolutional and LSTM layers. Simulations show up to 12% spectral efficiency gain over conventional pilot-based systems, with under 200k multiply-accumulate operations suitable for on-board satellite implementation.
By Bruno De Filippo, Carla Amatetti, Alessandro Vanelli-Coralli
Federated Learning (FL) over 5G cellular networks protects raw data but remains vulnerable to side-channel leakage. Prior fingerprinting attacks assume packet-level network visibility, an assumption that does not hold at the 5G Physical (PHY) layer, where user payloads are encrypted and Radio Network Temporary Identifiers (RNTIs) may change over time.
The Vienna 4G/5G Drive-Test Dataset is a city‑scale open dataset of georeferenced LTE and 5G NR measurements collected across Vienna, Austria. It combines passive wideband scanner observations with active handset logs, offering complementary network‑side and user‑side views of deployed radio access networks. The dataset includes inferred base‑station deployment descriptors, high‑resolution building and terrain models, and is organized into scanner, handset, estimated cell information, and city‑model components to support reproducible benchmarking in environment‑aware learning, propagation modeling, coverage analysis, and ray‑tracing calibration workflows.
By Wilfried Wiedner, Lukas Eller, Mariam Mussbah, Dominik R\"ossler, Valerian Maresch, Philipp Svoboda, Markus Rupp
arXiv:2608. 14591v1 Announce Type: cross Abstract: The integration of artificial intelligence (AI) and wireless communications is widely regarded as a core objective of sixth-generation (6G) systems.
By Shugong Xu, Jun Jiang, Yuan Gao