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.
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).
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.
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.
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.
arXiv:2606. 06261v1 Announce Type: cross Abstract: O-RAN enables a disaggregated baseband stack with programmable functions that communicate over standardized open interfaces.
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.
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.
arXiv:2607. 15469v1 Announce Type: cross Abstract: Federated Learning (FL) over 5G cellular networks protects raw data but remains vulnerable to side-channel leakage.
Reliable access selection in dynamic and heterogeneous indoor-outdoor environments is challenging because instantaneous radio measurements alone cannot capture future QoS degradation caused by mobilit...
arXiv:2607. 18522v1 Announce Type: new Abstract: Mobile network operators monitor aggregated traffic volumes to assess the operational health of core network infrastructure.
arXiv:2607. 28665v1 Announce Type: cross Abstract: Automated driving systems (ADSs) are becoming ubiquitous.
arXiv:2607. 08978v1 Announce Type: new Abstract: Distributed IoT systems generate multivariate time-series streams for monitoring physical assets, servers, and embedded sensing platforms.