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

TL;DR

This paper proposes a deep reinforcement learning approach using DDPG to optimize age-of-information in HAPS-enabled V2X networks, enhancing communication reliability and information freshness for autonomous vehicle systems.

Contribution

It introduces a novel DDPG-based method for AoI-aware resource allocation in HAPS-V2X networks, enabling decentralized learning and improved network performance.

Findings

Enhanced AoI performance in simulations

Improved network reliability for autonomous vehicles

Potential for scalable HAPS-supported communication solutions

Abstract

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.