mlsanityData to 14 Oct 2026

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

arXiv 2505.06978 ↗First posted 11 May 2025cs.LGcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 19 of its 45 references that are in the map. First posted on arXiv 11 May 2025.

Builds on

19 of 45 references.

References · 19

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7 more
  1. 2022Autonomous Platoon Control with Integrated Deep Reinforcement Learning and Dynamic Programmingcited by 5
  2. 2019Investigating Value of Information in Future Vehicular Communicationscited by 4
  3. 2023Multi-Timescale Control and Communications with Deep Reinforcement Learning -- Part II: Control-Aware Radio Resource Allocationcited by 4
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  6. 2020Fairness for Freshness: Optimal Age of Information Based OFDMA Scheduling with Minimal Knowledgecited by 1
  7. 2024Digital Retina for IoV Towards 6G: Architecture, Opportunities, and Challengescited by 1

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  1. 2025VoI-Driven Joint Optimization of Control and Communication in Vehicular Digital Twin Network
  2. 2022Reinforcement Learning for Joint V2I Network Selection and Autonomous Driving Policies
  3. 2025A Survey on the Role of Artificial Intelligence and Machine Learning in 6G-V2X Applications
  4. 2025AoI-Aware Resource Allocation with Deep Reinforcement Learning for HAPS-V2X Networks
  5. 2024DRL-Based RAT Selection in a Hybrid Vehicular Communication Network
  6. 2024Graph Neural Networks and Deep Reinforcement Learning Based Resource Allocation for V2X Communications
  7. 2024Event-Triggered Reinforcement Learning Based Joint Resource Allocation for Ultra-Reliable Low-Latency V2X Communications
  8. 2024Joint Channel Selection using FedDRL in V2X