mlsanityData to 7 Oct 2026

Deep-Reinforcement-Learning-Based AoI-Aware Resource Allocation for RIS-Aided IoV Networks

arXiv 2406.11245 ↗First posted 17 Jun 2024cs.LG cs.DC cs.NIcited by 12

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Abstract (arXiv)

Reconfigurable Intelligent Surface (RIS) is a pivotal technology in communication, offering an alternative path that significantly enhances the link quality in wireless communication environments. In this paper, we propose a RIS-assisted internet of vehicles (IoV) network, considering the vehicle-to-everything (V2X) communication method. In addition, in order to improve the timeliness of vehicle-to-infrastructure (V2I) links and the stability of vehicle-to-vehicle (V2V) links, we introduce the age of information (AoI) model and the payload transmission probability model. Therefore, with the objective of minimizing the AoI of V2I links and prioritizing transmission of V2V links payload, we construct this optimization problem as an Markov decision process (MDP) problem in which the BS serves as an agent to allocate resources and control phase-shift for the vehicles using the soft actor-critic (SAC) algorithm, which gradually converges and maintains a high stability. A AoI-aware joint vehicular resource allocation and RIS phase-shift control scheme based on SAC algorithm is proposed and simulation results show that its convergence speed, cumulative reward, AoI performance, and payload transmission probability outperforms those of proximal policy optimization (PPO), deep deterministic policy gradient (DDPG), twin delayed deep deterministic policy gradient (TD3) and stochastic algorithms.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 28 of its 84 references that are in the map. First posted on arXiv 17 Jun 2024.

Builds on

28 of 84 references.

References · 28

  1. 2018Soft Actor-Critic Algorithms and Applicationscited by 1,179
  2. 2020Reconfigurable Intelligent Surface Assisted Multiuser MISO Systems Exploiting Deep Reinforcement Learningcited by 38
  3. 2019Wireless Communications Through Reconfigurable Intelligent Surfacescited by 35
  4. 2020Reconfigurable Intelligent Surfaces: Principles and Opportunitiescited by 34
  5. 2021A Tutorial on 5G NR V2X Communicationscited by 29
  6. 2022Mobility-Aware Cooperative Caching in Vehicular Edge Computing Based on Asynchronous Federated and Deep Reinforcement Learningcited by 28
  7. 2020Smart Radio Environments Empowered by Reconfigurable Intelligent Surfaces: How it Works, State of Research, and Road Aheadcited by 26
  8. 2022High stable and accurate vehicle selection scheme based on federated edge learning in vehicular networkscited by 26
  9. 2023Generative AI-enabled Vehicular Networks: Fundamentals, Framework, and Case Studycited by 24
  10. 2022Towards V2I Age-aware Fairness Access: A DQN Based Intelligent Vehicular Node Training and Test Methodcited by 23
  11. 2023Delay-sensitive Task Offloading in Vehicular Fog Computing-Assisted Platoonscited by 23
  12. 2023URLLC-Awared Resource Allocation for Heterogeneous Vehicular Edge Computingcited by 22
16 more
  1. 2021AoI-Aware Resource Allocation for Platoon-Based C-V2X Networks via Multi-Agent Multi-Task Reinforcement Learningcited by 17
  2. 2024Cooperative Edge Caching Based on Elastic Federated and Multi-Agent Deep Reinforcement Learning in Next-Generation Networkcited by 17
  3. 2024Semantic-Aware Resource Allocation Based on Deep Reinforcement Learning for 5G-V2X HetNetscited by 17
  4. 2024Knowledge-Driven Deep Learning Paradigms for Wireless Network Optimization in 6Gcited by 15
  5. 2020A Vision of C-V2X: Technologies, Field Testing and Challenges with Chinese Developmentcited by 11
  6. 2021Decentralized Power Allocation for MIMO-NOMA Vehicular Edge Computing Based on Deep Reinforcement Learningcited by 10
  7. 2019Age of Information-Aware Radio Resource Management in Vehicular Networks: A Proactive Deep Reinforcement Learning Perspectivecited by 9
  8. 2020Reconfigurable Intelligent Surfaces for Wireless Communications: Principles, Challenges, and Opportunitiescited by 8
  9. 2021Reconfigurable Intelligent Surface Enabled Vehicular Communication: Joint User Scheduling and Passive Beamformingcited by 5
  10. 2022Asynchronous Federated Learning for Edge-assisted Vehicular Networkscited by 5
  11. 2019Optimized Age of Information Tail for Ultra-Reliable Low-Latency Communications in Vehicular Networkscited by 4
  12. 2021Reconfigurable Intelligent Surface-assisted Multi-UAV Networks: Efficient Resource Allocation with Deep Reinforcement Learningcited by 4
  13. 2022Deep Deterministic Policy Gradient to Minimize the Age of Information in Cellular V2X Communicationscited by 4
  14. 2021Reconfigurable Intelligent Surface Assisted Device-to-Device Communicationscited by 2
  15. 2021Age of Information Optimization in a RIS-Assisted Wireless Networkcited by 1
  16. 2023Sum Rate Maximization under AoI Constraints for RIS-Assisted mmWave Communicationscited by 1

Built on by

Cited by year

Citing papers per year: 2024: 7; 2025: 5.

12 papers.

  1. 2024DRL-Based Resource Allocation for Motion Blur Resistant Federated Self-Supervised Learning in IoVcited by 7
  2. 2024DRL-Based Optimization for AoI and Energy Consumption in C-V2X Enabled IoVcited by 6
  3. 2024Semantic-Aware Resource Management for C-V2X Platooning via Multi-Agent Reinforcement Learningcited by 5
  4. 2025A Survey on Semantic Communications in Internet of Vehiclescited by 3
  5. 2024A Comprehensive Survey on Joint Resource Allocation Strategies in Federated Edge Learningcited by 1
  6. 2025Federated Learning Assisted Edge Caching Scheme Based on Lightweight Architecture DDPMcited by 1
  7. 2024Resource Allocation for Twin Maintenance and Computing Task Processing in Digital Twin Vehicular Edge Computing Networkcited by 0
  8. 2024Blockchain-Enabled Variational Information Bottleneck for Data Extraction Based on Mutual Information in Internet of Vehiclescited by 0
  9. 2024V2X-Assisted Distributed Computing and Control Framework for Connected and Automated Vehicles under Ramp Merging Scenariocited by 0
  10. 2025Enhanced SPS Velocity-adaptive Scheme: Access Fairness in 5G NR V2I Networkscited by 0
  11. 2025PPO-Based Vehicle Control for Ramp Merging Scheme Assisted by Enhanced C-V2Xcited by 0
  12. 2025A Survey of Freshness-Aware Wireless Networking with Reinforcement Learningcited by 0