A Joint Reinforcement Learning Scheduling and Compression Framework for Teleoperated Driving

TL;DR

This paper presents a novel joint reinforcement learning framework that optimizes data compression and scheduling in teleoperated driving over 6G networks, improving latency and reliability in constrained environments.

Contribution

It introduces an integrated RL approach for simultaneous optimization of data compression and scheduling, including a meta-learning agent for adaptive strategy selection.

Findings

Integrated RL agents outperform standalone models in simulations.

The framework reduces latency and improves data transmission efficiency.

Meta-learning enhances adaptability to network conditions.

Abstract

Teleoperated driving (TD) is envisioned as a key application of future sixth generation (6G) networks. In this paradigm, connected vehicles transmit sensor-perception data to a remote (software) driver, which returns driving control commands to enhance traffic efficiency and road safety. This scenario imposes to maintain reliable and low-latency communication between the vehicle and the remote driver. To this aim, a promising solution is Predictive Quality of Service (PQoS), which provides mechanisms to estimate possible Quality of Service (QoS) degradation, and trigger timely network corrective actions accordingly. In particular, Reinforcement Learning (RL) agents can be trained to identify the optimal PQoS configuration. In this paper, we develop and implement two integrated RL agents that jointly determine (i) the optimal compression configuration for TD sensor data to balance the trade-off between transmission efficiency and data quality, and (ii) the optimal scheduling configuration to minimize the end-to-end latency by allocating radio resources according to different priority levels. We prove via full-stack ns-3 simulations that our integrated agents can deliver superior performance than any standalone model that only optimizes either compression or scheduling, especially in constrained or congested networks. While these agents can be deployed using either centralized or decentralized learning, we further propose a new meta-learning agent that dynamically selects the most appropriate strategy between the two based on current network conditions and application requirements.