Scholarlink

<p>With the rapid advancements in Space-Air-Ground-Sea Integrated Networks (SAGSIN), optimizing intelligent network resource allocation has become a pivotal research problem. Effective resource allocation can significantly enhance network performance, ensure quality of service (QoS) for users, and optimize resource utilization. To tackle this challenge, this paper proposes a network resource allocation optimization algorithm based on reinforcement learning, specifically designed to guarantee user QoS. Simulation results demonstrate that as the number of users increases, the proposed allocation algorithm, Q-DER, outperforms baseline algorithms, including random algorithms, greedy algorithms, the Q-DR algorithm and the Q-DE algorithm, in terms of average delay, access success rate, access energy consumption, and resource utilization rate. Furthermore, the proposed Q-DER algorithm significantly mitigates congestion in wireless resource-constrained scenarios. A comparative analysis of user satisfaction degree reveals that the proposed Q-DER algorithm performs better in guaranteeing user QoS in complex network environments.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

QoS-driven reinforcement learning-based resource allocation in space–air–ground–sea integrated networks

  • 期刊论文

  • 发表日期：

    2025年8月29日

查看全文
  • Bing Tang,
  • Jiayao Li,
  • Yujun Cao,
  • Qing Yang

摘要

With the rapid advancements in Space-Air-Ground-Sea Integrated Networks (SAGSIN), optimizing intelligent network resource allocation has become a pivotal research problem. Effective resource allocation can significantly enhance network performance, ensure quality of service (QoS) for users, and optimize resource utilization. To tackle this challenge, this paper proposes a network resource allocation optimization algorithm based on reinforcement learning, specifically designed to guarantee user QoS. Simulation results demonstrate that as the number of users increases, the proposed allocation algorithm, Q-DER, outperforms baseline algorithms, including random algorithms, greedy algorithms, the Q-DR algorithm and the Q-DE algorithm, in terms of average delay, access success rate, access energy consumption, and resource utilization rate. Furthermore, the proposed Q-DER algorithm significantly mitigates congestion in wireless resource-constrained scenarios. A comparative analysis of user satisfaction degree reveals that the proposed Q-DER algorithm performs better in guaranteeing user QoS in complex network environments.