mlsanityData to 14 Oct 2026

Joint Channel Selection using FedDRL in V2X

arXiv 2410.20687 ↗First posted 3 Oct 2024eess.SP cs.LGcited by 0

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

Abstract (arXiv)

Vehicle-to-everything (V2X) communication technology is revolutionizing transportation by enabling interactions between vehicles, devices, and infrastructures. This connectivity enhances road safety, transportation efficiency, and driver assistance systems. V2X benefits from Machine Learning, enabling real-time data analysis, better decision-making, and improved traffic predictions, making transportation safer and more efficient. In this paper, we study the problem of joint channel selection, where vehicles with different technologies choose one or more Access Points (APs) to transmit messages in a network. In this problem, vehicles must learn a strategy for channel selection, based on observations that incorporate vehicles' information (position and speed), network and communication data (Signal-to-Interference-plus-Noise Ratio from past communications), and environmental data (road type). We propose an approach based on Federated Deep Reinforcement Learning (FedDRL), which enables each vehicle to benefit from other vehicles' experiences. Specifically, we apply the federated Proximal Policy Optimization (FedPPO) algorithm to this task. We show that this method improves communication reliability while minimizing transmission costs and channel switches. The efficiency of the proposed solution is assessed via realistic simulations, highlighting the potential of FedDRL to advance V2X technology.

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 9 of its 33 references that are in the map. First posted on arXiv 3 Oct 2024.

Builds on

9 of 33 references.

References · 9

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 2,026
  3. 20206G for Vehicle-to-Everything (V2X) Communications: Enabling Technologies, Challenges, and Opportunitiescited by 24
  4. 2021AoI-Aware Resource Allocation for Platoon-Based C-V2X Networks via Multi-Agent Multi-Task Reinforcement Learningcited by 18
  5. 2020Deep Reinforcement Learning Based Mode Selection and Resource Allocation for Cellular V2X Communicationscited by 14
  6. 2023Multi-Agent Reinforcement Learning: Methods, Applications, Visionary Prospects, and Challengescited by 5
  7. 2022Variance Reduction for Policy-Gradient Methods via Empirical Variance Minimizationcited by 4
  8. 2023Deep Reinforcement Learning based Joint Spectrum Allocation and Configuration Design for STAR-RIS-Assisted V2X Communicationscited by 3
  9. 2024Rescale-Invariant Federated Reinforcement Learning for Resource Allocation in V2X Networkscited by 1

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  2. 2024Graph Neural Networks and Deep Reinforcement Learning Based Resource Allocation for V2X Communications
  3. 2025A Survey on the Role of Artificial Intelligence and Machine Learning in 6G-V2X Applications
  4. 2024Communication-Aware Consistent Edge Selection for Mobile Users and Autonomous Vehicles
  5. 2022Reinforcement Learning for Joint V2I Network Selection and Autonomous Driving Policies
  6. 2025Learning Value of Information towards Joint Communication and Control in 6G V2X
  7. 2025VoI-Driven Joint Optimization of Control and Communication in Vehicular Digital Twin Network
  8. 2024Event-Triggered Reinforcement Learning Based Joint Resource Allocation for Ultra-Reliable Low-Latency V2X Communications