# AoI-Aware Resource Allocation with Deep Reinforcement Learning for HAPS-V2X Networks

arXiv: https://arxiv.org/abs/2508.00011 · HTML: https://mlsanity.com/paper/2508.00011/

## 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.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 7 of 7)

- [6G for Vehicle-to-Everything (V2X) Communications: Enabling Technologies, Challenges, and Opportunities](https://mlsanity.com/paper/2012.07753/) (2020)
- [A Vision and Framework for the High Altitude Platform Station (HAPS) Networks of the Future](https://mlsanity.com/paper/2007.15088/) (2020)
- [Caching and Computation Offloading in High Altitude Platform Station (HAPS) Assisted Intelligent Transportation Systems](https://mlsanity.com/paper/2106.14928/) (2021)
- [HAPS-ITS: Enabling Future ITS Services in Trans-Continental Highways](https://mlsanity.com/paper/2105.04756/) (2021)
- [Handoff-Aware Distributed Computing in High Altitude Platform Station (HAPS)-Assisted Vehicular Networks](https://mlsanity.com/paper/2305.04196/) (2023)
- [UxNB-Enabled Cell-Free Massive MIMO with HAPS-Assisted Sub-THz Backhauling](https://mlsanity.com/paper/2201.07379/) (2022)
- [Link Budget Analysis for Reconfigurable Smart Surfaces in Aerial Platforms](https://mlsanity.com/paper/2008.12334/) (2020)

## 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.