Towards Decentralized Predictive Quality of Service in Next-Generation Vehicular Networks
This addresses safety-critical communication issues for teleoperated vehicles, but it is incremental as it builds on existing PQoS concepts with RL adaptations.
The paper tackled the problem of ensuring low latency and reliability for safety in teleoperated driving by designing a reinforcement learning agent for Predictive Quality of Service (PQoS) in vehicular networks, demonstrating through simulations that decentralized and federated learning offer a good trade-off between convergence time and reliability.
To ensure safety in teleoperated driving scenarios, communication between vehicles and remote drivers must satisfy strict latency and reliability requirements. In this context, Predictive Quality of Service (PQoS) was investigated as a tool to predict unanticipated degradation of the Quality of Service (QoS), and allow the network to react accordingly. In this work, we design a reinforcement learning (RL) agent to implement PQoS in vehicular networks. To do so, based on data gathered at the Radio Access Network (RAN) and/or the end vehicles, as well as QoS predictions, our framework is able to identify the optimal level of compression to send automotive data under low latency and reliability constraints. We consider different learning schemes, including centralized, fully-distributed, and federated learning. We demonstrate via ns-3 simulations that, while centralized learning generally outperforms any other solution, decentralized learning, and especially federated learning, offers a good trade-off between convergence time and reliability, with positive implications in terms of privacy and complexity.