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

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.

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

The paper titled "Learning Value of Information Towards Joint Communication and Control in 6G V2X" introduces a systematic framework for assessing the Value of Information (VoI) in optimizing communication systems in 6G networks, specifically for Connected Autonomous Vehicles (CAVs). The authors propose a comprehensive methodology for evaluating and leveraging VoI to improve decision-making processes in joint communication and control domains. As 6G networks evolve, providing enhanced support for Vehicle-to-Everything (V2X) communication becomes vital, given CAVs' reliance on such information for improved autonomous operations.

Theoretical Foundations

The authors introduce a Sequential Stochastic Decision Process (SSDP) model, expanding upon the traditional Markov Decision Processes (MDPs) by integrating exogenous information that influences both state transitions and rewards. This model is pivotal in defining and assessing the VoI, which plays a crucial role in enabling coordinated decisions regarding communication and control. The importance of information sharing in CAV systems is underscored by the ability to augment control strategies through additional external data. By investigating the specific influence of certain information elements on decision-making, the authors bridge the gap that traditional MDP models leave unexplored.

Categories of VoI

The paper categorizes VoI into utility-based and information theory-based approaches:

1. Utility-based VoI is divided into Expected Cumulative VoI (EVoI) and Immediate VoI (IVoI). EVoI quantifies long-term performance differences between policies based on partial versus full information, while IVoI assesses single instance gains.

2. Information Theory-based VoI quantifies improvements in prediction capabilities for future states and rewards brought by additional information. These metrics are crucial for accurate modeling and subsequent optimization of V2X communications.

Implications in Communication Optimization

The practical application of VoI modeling is demonstrated by addressing the three key decision areas in V2X communication: "What," "When," and "How" to communicate. These decisions can occur at varying time scales—every control interval or across multiple intervals. The authors propose an optimization framework where VoI-oriented metrics are integrated into decision-making processes. This integration ensures that communication resources are allocated efficiently, balancing traditional throughput optimization against minimizing control performance degradation due to communication imperfections.

Contribution to Future Research

This study sets the stage for future explorations into the unified design of control and communication systems in CAV networks. It highlights the need to further refine VoI estimation techniques and enhance communication state variable construction for SSDP models. Additionally, the challenge of sparse rewards in learning efficient communication policies offers rich avenues for research.

In conclusion, the paper provides valuable insights into the interconnected domains of communication and control in V2X environments, laying a solid foundation for further advancements in optimizing 6G networks for autonomous systems. The methodologies proposed for assessing and utilizing VoI pave the way for deploying more effective and adaptive communication mechanisms crucial for the success of CAVs.