mlsanityData to 7 Oct 2026

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

arXiv 2109.09376 ↗First posted 20 Sep 2021cs.NI cs.LGcited by 3

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Abstract (arXiv)

Recent advances in software, hardware, computing and control have fueled significant progress 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 time frame, and foresee whether or not the network will be able to fulfill the agreed Quality of Service (QoS). If not, appropriate countermeasures should be taken to satisfy the application requirements. Along these lines, in this paper 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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 3 of its 20 references that are in the map. First posted on arXiv 20 Sep 2021.

Builds on

3 of 20 references.

References · 3

  1. 2019RadioUNet: Fast Radio Map Estimation with Convolutional Neural Networkscited by 126
  2. 2019Towards 6G Networks: Use Cases and Technologiescited by 13
  3. 2019An Adaptive Broadcasting Strategy for Efficient Dynamic Mapping in Vehicular Networkscited by 3

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Cited by year

Citing papers per year: 2021: 1; 2022: 2.

3 papers.