Papers for

mobile network planners

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Mitigating network changes improves teleoperation quality for autonomous vehicles

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

Abstract: Teleoperation serves as the fallback solution to autonomous driving but reliable functions of the teleoperation require a certain amount of mobile network resources, which cannot be guaranteed at all times. Therefore, predictive quality of service (pQoS) is introduced as a concept to increase the resilience of the teleoperation. In this paper, based on a data measurement campaign, we propose a prediction framework to prediction two important network KPIs of teleoperation: uplink data-rate and round-trip latency. Furthermore, we introduce a method to alleviate the performance degradation of machine-learning-based prediction models on previously unseen data due to concept drift by incorporating historic data into the prediction pipeline. Additionally, we introduce the metric of critical scenario detection to evaluate the prediction performance specifically for teleoperation.

Tue 6 OctRoboticsArtificial Intelligence

The gist

Controlling driverless cars remotely relies on good and stable mobile network connections, which can change unexpectedly. The authors created a method to predict two important network factors—how fast data moves uplink and the delay in communication—and keep predictions accurate even when network conditions shift. They use past network data to help the prediction model adjust to new situations. They also developed a way to spot critical moments when network performance might threaten teleoperation safety.

Open → 2610.08297v1