Offshore Optical-wireless Integrated Access Network Deployment Algorithm based on MHA-MAD
【Objective】With the rapid growth of business volume in the nearshore area, the demand for bandwidth is showing an exponential growth trend. The resources of the 5th Generation Mobile Communication Technology (5G) and Beyond-5G (B5G) Next Generation Radio Access Network (NG-RAN) that carry the busine...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
《光通信研究》编辑部
2025-08-01
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| Series: | Guangtongxin yanjiu |
| Subjects: | |
| Online Access: | http://www.gtxyj.com.cn/zh/article/doi/10.13756/j.gtxyj.2025.250076/ |
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| Summary: | 【Objective】With the rapid growth of business volume in the nearshore area, the demand for bandwidth is showing an exponential growth trend. The resources of the 5th Generation Mobile Communication Technology (5G) and Beyond-5G (B5G) Next Generation Radio Access Network (NG-RAN) that carry the business are about to be exhausted. Wavelength Division Multiplexing-Passive Optical Network (WDM-PON), with its advantages such as high bandwidth, has become an effective solution to support 5G/B5G NG-RAN. However, the complex and variable offshore environment poses severe challenges for the deployment of WDM-PON networks. These challenges include high deployment costs, substantial path losses, and harsh underwater conditions. There is an urgent need to optimize network deployment strategies to reduce costs, risks, and transmission losses in order to build a network that is suitable for the offshore environment.【Methods】This study proposes a Multi-Head Attention enhanced Multi-Agent Deep Q-Network (MHA-MAD) algorithm. It efficiently extracts key features of the network environment using multi-head attention mechanism and assigns dynamic weights to different features, thereby improving modeling accuracy. Simultaneously, the multi-agent structure allows multiple agents to collaborate and make synchronized decisions within a shared network environment, promoting global optimization in network deployment.【Results】Compared to other benchmarks, the MHA-MAD algorithm improves performance in network deployment by nearly 42%, with results approaching the theoretical optimum. Furthermore, compared to multi-agent Deep Q-Network (DQN) method without the multi-head attention, MHA-MAD algorithm improves the performance by nearly 8% in the joint optimization objective of minimizing overall network deployment costs, node power consumption, link attenuation, and network deployment risk probabilities.【Conclusion】MHA-MAD provides new insights for the deployment and optimization of WDM-PON to support 5G/B5G NG-RAN in offshore scenarios. |
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| ISSN: | 1005-8788 |