Recent advances in software, hardware, computing, and control have fueled significant prog-ress in the field of autonomous systems. Notably, autonomous machines should continuously estimate how the scenario in which they move and operate will evolve within a predefined timeframe, and foresee whether or not the network will be able to fulfill the agreed quality of service (QoS). If not, appropriate countermea-sures should be taken to satisfy the application requirements. Along these lines, in this article we present possible methods to enable predictive QoS (PQoS) in autonomous systems, and discuss which use cases will particularly benefit from network prediction. Then we shed light on the challenges in the field that are still open for future research. As a case study, we demonstrate whether machine learning can facilitate PQoS in a teleoperated-driving-like use case as a function of different measurement signals.

Predictive Quality of Service: The Next Frontier for Fully Autonomous Systems

Giordani, Marco;Zorzi, Michele
2021

Abstract

Recent advances in software, hardware, computing, and control have fueled significant prog-ress in the field of autonomous systems. Notably, autonomous machines should continuously estimate how the scenario in which they move and operate will evolve within a predefined timeframe, and foresee whether or not the network will be able to fulfill the agreed quality of service (QoS). If not, appropriate countermea-sures should be taken to satisfy the application requirements. Along these lines, in this article we present possible methods to enable predictive QoS (PQoS) in autonomous systems, and discuss which use cases will particularly benefit from network prediction. Then we shed light on the challenges in the field that are still open for future research. As a case study, we demonstrate whether machine learning can facilitate PQoS in a teleoperated-driving-like use case as a function of different measurement signals.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11577/3414766
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