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:2606. 06663v1 Announce Type: new Abstract: Future AI-integrated Radio Access Networks (AI-RAN) will combine open programmability with learning-enabled xApps, rApps, and control functions that act on shared parameters and key performance indicators (KPIs).
By Christie Djidjev, Nicholas Kaminski
The paper introduces Confounding-Valid Counterfactual Conformal Inference (CV‑CCI), a method that merges abundant observational telemetry with limited randomized data to answer network operators’ ‘what‑if’ questions about key performance indicators (KPIs). CV‑CCI uses the General Synthetic‑Powered Inference principle to maintain finite‑sample coverage guarantees even when hidden confounding is present, while producing tighter prediction sets than existing baselines. Experiments on two radio access network control tasks demonstrate the method’s validity under hidden confounding and its improved efficiency.
By Abdessamed Qchohi, Jessica Moysen Cortes, Matteo Zecchin
The paper introduces a Statistical Ensemble Method (SEM) for detecting radio‑frequency jamming in Open Radio Access Network (O‑RAN) environments. SEM combines adaptive Median Absolute Deviation, count‑based anomaly detection, and abrupt change detection, operating in both cold‑start and supervised modes. Experiments on 25 hours of data from eight heterogeneous 5G testbeds show that SEM retains recall under distribution shifts where machine‑learning models falter, and its parameters can be deployed as an O‑RAN rApp for continuous refinement.
By Sneihil Gopal, Fernando J. Cintr\'{o}n, Richard A. Rouil
Reliable reinforcement learning (RL) agents must maintain operational integrity amidst sensor malfunctions, dynamic disturbances, and slow environmental shifts. The detection of out-of-distribution conditions is pivotal to determining when an agent's observations, transitions, or trajectory dynamics deviate from the assumptions underpinning its policy training.
arXiv:2607. 12523v1 Announce Type: cross Abstract: Reliable reinforcement learning (RL) agents must maintain operational integrity amidst sensor malfunctions, dynamic disturbances, and slow environmental shifts.
By Emil Mittag, Richard Dazeley, Peter Vamplew