用深度强化学习优化高空平台车联网络的信息时效性。
AoI-Aware Resource Allocation with Deep Reinforcement Learning for HAPS-V2X Networks
- 采用DDPG算法实现无中心化动态资源分配
- 显著提升车辆编队系统的数据新鲜度
- 适合研究6G低时延通信与智能交通系统的人
第六代（6G）网络旨在满足自动驾驶等安全关键应用对超可靠低时延通信（HRLLC）的需求。将非地面网络（NTN）融入6G基础设施可增强网络冗余，在极端条件下保障通信连续性。其中，高空平台站（HAPS）具有广覆盖和低延迟优势，尤其在农村及基础设施受限区域，能提升通信可靠性与信息新鲜度。本文提出基于深度确定性策略梯度（DDPG）的强化学习方法，动态优化HAPS支持的车联网络（V2X）中的信息年龄（AoI）。该方法无需集中协调即可实现独立学习，有效提升信息新鲜度与整体网络可靠性。结果表明，结合HAPS与基于DDPG的学习机制，可高效实现面向车队自动驾驶系统的AoI感知资源分配。
原文摘要 · Abstract (English)
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.
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