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

• URL: http://arxiv.org/abs/2508.00011v1
• Date: Mon, 21 Jul 2025 10:11:12 GMT
• Title: AoI-Aware Resource Allocation with Deep Reinforcement Learning for HAPS-V2X Networks
• Authors: Ahmet Melih Ince, Ayse Elif Canbilen, Halim Yanikomeroglu,
• Abstract summary: Sixth-generation (6G) networks are designed to meet the hyper-reliable and low-latency communication requirements of safety-critical applications such as autonomous driving.<n>Integrating non-terrestrial networks (NTN) into the 6G infrastructure brings redundancy to the network, ensuring continuity of communications even under extreme conditions.<n>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.
• Score: 22.47860804880012
• License: http://creativecommons.org/publicdomain/zero/1.0/
• 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.

Related papers

• Security-Aware Joint Sensing, Communication, and Computing Optimization in Low Altitude Wireless Networks [83.84711311344918]
  Integrated sensing, communications, and computing (I SCC) is one of the core parts of Low-Altitude Wireless Networks (LAWNs)<n>This paper studies joint performance optimization of I SCC while considering secrecyness of the communications.<n>We propose a deep Q-network (DQN)-based multi-objective evolutionary algorithm, which adaptively selects evolutionary operators according to the evolving optimization objectives.
  arXiv  Detail & Related papers  (2025-11-03T11:06:41Z)
• Bridging Earth and Space: A Survey on HAPS for Non-Terrestrial Networks [0.0]
  HAPS are emerging as key enablers in the evolution of 6G wireless networks, bridging terrestrial and non-terrestrial infrastructures.<n>This survey delivers a comprehensive overview of HAPS use cases, technologies, and integration strategies within the 6G ecosystem.
  arXiv  Detail & Related papers  (2025-10-22T16:22:31Z)
• Graph Neural Networks and Deep Reinforcement Learning Based Resource Allocation for V2X Communications [43.443526528832145]
  This paper proposes a method that integrates Graph Neural Networks (GNN) with Deep Reinforcement Learning (DRL) to address this challenge.<n>By constructing a dynamic graph with communication links as nodes, the model aims to ensure a high success rate for V2V communication.<n>The proposed method retains the global feature learning capabilities of GNN and supports distributed network deployment.
  arXiv  Detail & Related papers  (2024-07-09T03:14:11Z)
• Deep-Reinforcement-Learning-Based AoI-Aware Resource Allocation for RIS-Aided IoV Networks [43.443526528832145]
  We propose a RIS-assisted internet of vehicles (IoV) network, considering the vehicle-to-everything (V2X) communication method.<n>In order to improve the timeliness of vehicle-to-infrastructure (V2I) links and the stability of vehicle-to-vehicle (V2V) links, we introduce the age of information (AoI) model and the payload transmission probability model.
  arXiv  Detail & Related papers  (2024-06-17T06:16:07Z)
• Enabling Intelligent Vehicular Networks Through Distributed Learning in the Non-Terrestrial Networks 6G Vision [0.5461938536945721]
  6G-enabled Intelligent Transportation System (ITS) is set to redefine conventional transportation networks with advanced intelligent services and applications. These technologies pose stringent requirements for latency, energy efficiency, and user data security. We introduce the concept of Federated Split Transfer Learning (FSTL) in joint air-ground networks for resource-constrained vehicular scenarios.
  arXiv  Detail & Related papers  (2023-09-07T22:18:21Z)
• Artificial Intelligence Empowered Multiple Access for Ultra Reliable and Low Latency THz Wireless Networks [76.89730672544216]
  Terahertz (THz) wireless networks are expected to catalyze the beyond fifth generation (B5G) era. To satisfy the ultra-reliability and low-latency demands of several B5G applications, novel mobility management approaches are required. This article presents a holistic MAC layer approach that enables intelligent user association and resource allocation, as well as flexible and adaptive mobility management.
  arXiv  Detail & Related papers  (2022-08-17T03:00:24Z)
• Machine Learning-Based User Scheduling in Integrated Satellite-HAPS-Ground Networks [82.58968700765783]
  Integrated space-air-ground networks promise to offer a valuable solution space for empowering the sixth generation of communication networks (6G) This paper showcases the prospects of machine learning in the context of user scheduling in integrated space-air-ground communications.
  arXiv  Detail & Related papers  (2022-05-27T13:09:29Z)
• Federated Learning over Wireless IoT Networks with Optimized Communication and Resources [98.18365881575805]
  Federated learning (FL) as a paradigm of collaborative learning techniques has obtained increasing research attention. It is of interest to investigate fast responding and accurate FL schemes over wireless systems. We show that the proposed communication-efficient federated learning framework converges at a strong linear rate.
  arXiv  Detail & Related papers  (2021-10-22T13:25:57Z)
• Path Design and Resource Management for NOMA enhanced Indoor Intelligent Robots [58.980293789967575]
  A communication enabled indoor intelligent robots (IRs) service framework is proposed. Lego modeling method is proposed, which can deterministically describe the indoor layout and channel state. The investigated radio map is invoked as a virtual environment to train the reinforcement learning agent.
  arXiv  Detail & Related papers  (2020-11-23T21:45:01Z)
• Cognitive Radio Network Throughput Maximization with Deep Reinforcement Learning [58.44609538048923]
  Radio Frequency powered Cognitive Radio Networks (RF-CRN) are likely to be the eyes and ears of upcoming modern networks such as Internet of Things (IoT) To be considered autonomous, the RF-powered network entities need to make decisions locally to maximize the network throughput under the uncertainty of any network environment. In this paper, deep reinforcement learning is proposed to overcome the shortcomings and allow a wireless gateway to derive an optimal policy to maximize network throughput.
  arXiv  Detail & Related papers  (2020-07-07T01:49:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.