Teleoperated Driving & V2X
A Reinforcement Learning-Based PQoS Framework for V2X Networks in NS-3
This paper presents an RL-based framework for Predictive Quality of Service (PQoS) in Vehicle-to-Everything (V2X) networks, specifically for 6G teleoperated driving scenarios. Utilizing Deep Q-Learning (DQN) and ns-3 simulations, the framework optimizes QoS and Quality of Experience (QoE) by dynamically managing data compression modes based on network conditions and performance metrics. The results demonstrate its superior ability to balance latency and data quality compared to baseline methods, ensuring reliable and responsive teleoperated driving.
Key AI-Driven Impact for Your Enterprise
Our analysis highlights the critical performance enhancements enabled by this RL-based PQoS framework, directly translating into operational efficiencies and improved reliability for V2X applications.
0%
Latency Reduction
0%
Reliability Improvement
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QoS/QoE Balance Score
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
90%
of DQL decisions prioritize optimal QoS/QoE balance, especially for Mode 1450, ensuring data accuracy and low latency for teleoperated driving.
RL-Based PQoS Operation Flow
Collect RAN & CN Metrics
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Construct State Representation
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RL Agent Decision (Compression Mode)
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Action Dissemination
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Optimize Network Configuration
| Feature | DQL (α=0.5) | Constant 1450 | Constant 1452 |
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| Latency Performance |
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| Resource Efficiency |
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Teleoperated Driving Resilience with DQL
A remote driver controls a Host Vehicle (HV) in Bologna, Italy, using sensor data transmitted via a 6G V2X network. The DQL agent dynamically adjusts data compression (e.g., opting for Mode 1450) to maintain critical 50ms latency and 99.999% reliability. When network congestion is predicted, the DQL agent proactively switches to a lower compression level, ensuring continuous, high-quality data flow for object detection and vehicle control, preventing system failures and enhancing driver safety.
Estimate Your V2X Network Optimization ROI
Quantify the potential savings and efficiency gains by implementing an AI-driven PQoS framework in your V2X operations.
Annual Cost Savings
$0
Annual Operational Hours Reclaimed
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Your AI Implementation Roadmap
A phased approach to integrating the PQoS framework, ensuring a smooth transition and maximum impact for your V2X infrastructure.
Phase 1: Network & Data Integration
Integrate ns-3 with existing V2X simulation environments, establish data pipelines for real-time RAN metrics (RSRP, SINR, latency), and deploy the TRAI entity within the gNB. Baseline performance analysis.
Phase 2: RL Model Training & Validation
Offline training of the DQL agent using historical and simulated data. Validate PQoS performance across various compression modes (1450, 1451, 1452) under diverse traffic and mobility patterns. Refine reward function parameters (α) to balance QoS/QoE.
Phase 3: Online Deployment & Continuous Optimization
Deploy the DQL agent in an online simulation for continuous adaptation. Implement A/B testing against static strategies. Monitor real-time QoS (latency, PRR) and QoE (Chamfer Distance) metrics. Implement federated learning for scalability and enhanced adaptability.
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