arXiv:2606. 06459v1 Announce Type: new Abstract: Next-generation wireless networks are expected to rely on multiple concurrent AI-driven control functions that optimize different network objectives simultaneously, particularly in AI-integrated and open radio access network architectures such as AI Radio Access Network (AI-RAN) and Open Radio Access Network (O-RAN).
By Christie Djidjev, Nicholas Kaminski
arXiv:2508. 00042v2 Announce Type: replace-cross Abstract: Machine learning models deployed in non-stationary environments degrade silently, since as the input distribution drifts their accuracy decays without an error signal and without labels to reveal it.
By Athanasios Tziouvaras, Carolina Fortuna, George Floros, Kostas Kolomvatsos, Panagiotis Sarigiannidis, Marko Grobelnik, Bla\v{z} Bertalani\v{c}
FedQoS is a federated learning framework that predicts future QoS failure probabilities for candidate access links in dynamic indoor‑outdoor environments, enabling reliable access‑node selection without centralizing user data. Each access node trains locally on its network logs, while a global QoS‑risk predictor is built through federated aggregation. Simulations using physics‑based synthetic datasets show that FedQoS reduces QoS‑failure rates compared to signal‑based and historical‑QoS heuristics, achieving near‑centralized performance even under non‑IID data conditions.
By Nguyen Van Thieu, Ti Ti Nguyen, Ons Aouedi, Zerihun Huruy, Vu Nguyen Ha, Symeon Chatzinotas
CellTwin, also known as NeuralEmu, is a machine‑learning‑based emulation framework that learns 5G scheduler behavior from high‑resolution network telemetry. It predicts resource block allocations and modulation schemes for multiple clients, reconstructs background traffic patterns, and runs as a high‑performance Linux middlebox. Compared to existing tools, it reduces emulation error by up to 57% for key real‑time applications such as web‑page load, WebRTC, and cloud gaming.
By Haoran Wan, Yaxiong Xie, Kyle Jamieson
arXiv:2606. 06261v1 Announce Type: cross Abstract: O-RAN enables a disaggregated baseband stack with programmable functions that communicate over standardized open interfaces.
By Francesco Spinelli, Esteban Municio, Pau Baguer, Gines Garcia-Aviles, Xavier Costa-Perez
The paper presents a significance‑based event‑detection method for AI‑enabled radio access networks, converting noisy continuous telemetry of control parameters and key performance indicators (KPIs) into binary indicators of control activity and KPI response. By constructing a closed‑loop telemetry generator with planted dependencies, the authors demonstrate that their procedure reliably identifies genuine control actions and corresponding KPI responses, outperforming alternative methods across varying background‑variation levels.
By Christie Djidjev, Nicholas Kaminski