Predictive Quality of Service (PQoS): The Next Frontier for Fully Autonomous Systems
This addresses the need for fully autonomous systems to maintain reliable network performance, but it is incremental as it builds on existing advances and focuses on discussing methods and challenges.
The paper tackles the problem of enabling predictive Quality of Service (PQoS) for autonomous systems to foresee network conditions and take countermeasures, demonstrating through a teleoperated-driving case study that machine learning can facilitate PQoS based on measurement signals.
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.