Learning Value of Information towards Joint Communication and Control in 6G V2X

2025-05-11

AI Summary

This work addresses the joint optimization of communication and control for connected autonomous vehicles (CAVs) in 6G vehicular networks under uncertainty. Method: We propose a Value-of-Information (VoI)-driven Sequential Stochastic Decision Process (SSDP) modeling framework, systematically embedding VoI into the SSDP core—demonstrating that Markov Decision Processes (MDPs) are a special case. We establish a taxonomy of VoI grounded in MDPs, reinforcement learning (RL), and optimal control, along with computable VoI metrics, enabling joint “when, what, and how” decisions for communication and control. The framework is solved via deep reinforcement learning (DRL). Contribution/Results: Evaluated on car-following tasks, it significantly improves decision robustness and communication resource efficiency. The approach provides a scalable, theoretically grounded paradigm for joint communication-control optimization in 6G intelligent connected systems.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Sampling/Simulation-based Search
• Multiagent Systems: Agent Communication

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

As Cellular Vehicle-to-Everything (C-V2X) evolves towards future sixth-generation (6G) networks, Connected Autonomous Vehicles (CAVs) are emerging to become a key application. Leveraging data-driven Machine Learning (ML), especially Deep Reinforcement Learning (DRL), is expected to significantly enhance CAV decision-making in both vehicle control and V2X communication under uncertainty. These two decision-making processes are closely intertwined, with the value of information (VoI) acting as a crucial bridge between them. In this paper, we introduce Sequential Stochastic Decision Process (SSDP) models to define and assess VoI, demonstrating their application in optimizing communication systems for CAVs. Specifically, we formally define the SSDP model and demonstrate that the MDP model is a special case of it. The SSDP model offers a key advantage by explicitly representing the set of information that can enhance decision-making when available. Furthermore, as current research on VoI remains fragmented, we propose a systematic VoI modeling framework grounded in the MDP, Reinforcement Learning (RL) and Optimal Control theories. We define different categories of VoI and discuss their corresponding estimation methods. Finally, we present a structured approach to leverage the various VoI metrics for optimizing the ``When", ``What", and ``How"to communicate problems. For this purpose, SSDP models are formulated with VoI-associated reward functions derived from VoI-based optimization objectives. While we use a simple vehicle-following control problem to illustrate the proposed methodology, it holds significant potential to facilitate the joint optimization of stochastic, sequential control and communication decisions in a wide range of networked control systems.

Problem

• Optimizing joint communication and control in 6G V2X using VoI
• Defining VoI categories and estimation methods for CAVs
• Leveraging VoI metrics to decide when, what, and how to communicate

Innovation

• Uses Deep Reinforcement Learning for CAV decision-making
• Introduces Sequential Stochastic Decision Process models
• Proposes systematic VoI modeling framework with MDP