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arXiv AI

Mitigating Concept Drift in QoS Prediction for Teleoperation of Autonomous Vehicles Using Historic Data

The paper proposes a prediction framework for two key network KPIs—uplink data‑rate and round‑trip latency—critical for teleoperation of autonomous vehicles. It introduces a method that incorporates historic data to mitigate concept drift in machine‑learning models, thereby improving prediction reliability on unseen data. Additionally, the authors present a metric for critical scenario detection to evaluate prediction performance specifically for teleoperation.

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Hugging Face Trending Papers
Aug 4

Predicting Deep Neural Network Training Outcomes from Early Training Telemetry

Large hyperparameter sweeps for deep neural networks spend substantial compute on configurations that are effectively doomed from the first few epochs. We study whether a single training run's own early telemetry - per-epoch loss, training accuracy, gradient signal-to-noise ratio, weight-norm growth, and an activation-saturation snapshot - together with its sampled hyperparameters, can predict that run's eventual outcome without reference to other runs.

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arXiv AI
Sep 7

ResLearn-XR: Residual Learning for Network Traffic and Quality-of-Experience-Aware Modeling in Extended Reality

ResLearn-XR is a residual learning framework designed to predict extended reality (XR) network traffic and estimate Quality-of-Experience (QoE) risk. It uses a two‑stage temporal learning structure: a base sequence prediction model followed by task‑specific residual components that operate in value space for traffic forecasting and in logit space for probabilistic QoE risk estimation. The framework introduces a Data Descriptor Algorithm (DDA) to convert packet‑level observables into frame‑timing‑aware descriptors and is evaluated on a newly constructed XR Traffic‑QoE dataset, achieving significant reductions in SMAPE for both traffic prediction and QoE‑risk estimation compared to single‑stage baselines.

By Yoga Suhas Kuruba Manjunath, Jie Gao, Lian Zhao
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arXiv Machine Learning
Jun 25

Cellular Predictions on the Move: What about Data?

arXiv:2606. 25709v1 Announce Type: new Abstract: Mobile cellular load forecasting is native to network resource optimization and delivery of services with reliability, latency and quality guarantees.

By Natalia Vesselinova, Pauliina Ilmonen
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arXiv Machine Learning
Jun 5

Event Detection for Parameter-to-KPI Dependency Learning for AI-RAN

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
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Hugging Face Trending Papers
Aug 10

Towards Collaborative Joint Perception and Prediction: Framework, Baseline Evaluation, and Deployment Perspectives

Connected Autonomous Vehicles (CAVs) increasingly exploit Vehicle-to-Everything (V2X) communication to exchange multi-source sensor information, enabling advanced Collaborative Perception (CP) capabilities. Extending beyond these capabilities, this work focuses on Collaborative Joint Perception and Prediction (Co-P&P), a paradigm that unifies CP with motion prediction to mitigate two persistent challenges: the accumulation of perception errors and visual occlusions.

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