AoI-Aware Resource Allocation with Deep Reinforcement Learning for HAPS-V2X Networks
This addresses resource allocation for safety-critical autonomous driving applications in 6G networks, but it is incremental as it applies an existing DDPG method to a new HAPS-V2X context.
The paper tackles dynamic optimization of age-of-information (AoI) in HAPS-enabled V2X networks using deep reinforcement learning (DDPG), improving information freshness and network reliability without centralized coordination.
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.