QoS-Aware multi-agent DDPG for adaptive edge service distribution in intelligent wireless communication networks
Effective service distribution management is essential in Intelligent Wireless Communication Networks to meet the increasing Quality of Service (QoS) demands across various applications. Traditional transmission strategies often prioritize high-QoS data, which can lead to access starvation for lower...
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| Главные авторы: | , , |
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| Формат: | Статья |
| Язык: | английский |
| Опубликовано: |
Elsevier
2025-09-01
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| Серии: | Ain Shams Engineering Journal |
| Предметы: | |
| Online-ссылка: | http://www.sciencedirect.com/science/article/pii/S2090447925002849 |
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| Итог: | Effective service distribution management is essential in Intelligent Wireless Communication Networks to meet the increasing Quality of Service (QoS) demands across various applications. Traditional transmission strategies often prioritize high-QoS data, which can lead to access starvation for lower-priority data in resource-constrained environments. To address this, we propose a QoS-aware adaptive service distribution strategy that balances the needs of high- and low-priority data without compromising the performance of either. Leveraging enhanced Multi-Agent Deep Deterministic Policy Gradient (e-MADDPG), our solution dynamically optimizes service distribution in mobile edge networks. By employing a gated recurrent unit-enhanced reinforcement learning framework, we enable intelligent agents to collaboratively decide channel access based on real-time traffic conditions. The proposed multi-criteria Decision-based Multi-channel Access algorithm allows high-priority data to defer access if necessary, improving the completion rates of lower-priority data. Furthermore, our method integrates network slicing and computation offloading to enhance service adaptability, ensuring efficient use of edge resources. Simulation results confirm that our framework significantly outperforms existing approaches in terms of channel utilization, QoS adherence, and overall network efficiency. |
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| ISSN: | 2090-4479 |