AoI-Aware Resource Allocation with Deep Reinforcement Learning for HAPS-V2X Networks
On the map: Deep reinforcement learning methods → RL for energy, networks and operations
Abstract (arXiv)
Sixth-generation (6G) networks are designed to meet the hyper-reliable and low-latency communication (HRLLC) requirements of safety-critical applications such as autonomous driving. Integrating non-terrestrial networks (NTN) into the 6G infrastructure brings redundancy to the network, ensuring continuity of communications even under extreme conditions. In particular, high-altitude platform stations (HAPS) stand out for their wide coverage and low latency advantages, supporting communication reliability and enhancing information freshness, especially in rural areas and regions with infrastructure constraints. In this paper, we present reinforcement learning-based approaches using deep deterministic policy gradient (DDPG) to dynamically optimize the age-of-information (AoI) in HAPS-enabled vehicle-to-everything (V2X) networks. The proposed method improves information freshness and overall network reliability by enabling independent learning without centralized coordination. The findings reveal the potential of HAPS-supported solutions, combined with DDPG-based learning, for efficient AoI-aware resource allocation in platoon-based autonomous vehicle 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 7 of its 13 references that are in the map. First posted on arXiv 21 Jul 2025.
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7 of 13 references.
References · 7
- 20206G for Vehicle-to-Everything (V2X) Communications: Enabling Technologies, Challenges, and Opportunitiescited by 24
- 2020A Vision and Framework for the High Altitude Platform Station (HAPS) Networks of the Futurecited by 15
- 2021Caching and Computation Offloading in High Altitude Platform Station (HAPS) Assisted Intelligent Transportation Systemscited by 6
- 2021HAPS-ITS: Enabling Future ITS Services in Trans-Continental Highwayscited by 3
- 2023Handoff-Aware Distributed Computing in High Altitude Platform Station (HAPS)-Assisted Vehicular Networkscited by 3
- 2022UxNB-Enabled Cell-Free Massive MIMO with HAPS-Assisted Sub-THz Backhaulingcited by 2
- 2020Link Budget Analysis for Reconfigurable Smart Surfaces in Aerial Platformscited by 1
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- 2025Learning Value of Information towards Joint Communication and Control in 6G V2X
- 2026Digital Twin-Assisted Adaptive Multi-Agent DRL for Intelligent Spectrum and Resource Management in Open-RAN UAV-Enabled 6G Networks
- 2024Non-orthogonal Age-Optimal Information Dissemination in Vehicular Networks: A Meta Multi-Objective Reinforcement Learning Approach
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