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Offshore Optical-wireless Integrated Access Network Deployment Algorithm based on MHA-MAD
- Study on Optical Communications Issue 4, (2025)
- 作者机构：
1.北京信息科技大学 智能通信与计算研究所，北京 102206
2.国防科技大学 信息系统工程全国重点实验室，长沙 410073
- 作者简介：
- 基金信息：
DOI：10.13756/j.gtxyj.2025.250076
CLC： TN929Received：05 March 2025，
Revised：21 May 2025，
Published：10 August 2025
- 稿件说明：
移动端阅览
李学华，郗童，王鑫，等. 基于MHA-MAD的近海光无线融合接入网络部署算法[J]. 光通信研究，2025(4): 250076.
Li X H, Xi T, Wang X, et al. Offshore Optical-wireless Integrated Access Network Deployment Algorithm based on MHA-MAD[J]. Study on Optical Communications, 2025(4): 250076.
李学华，郗童，王鑫，等. 基于MHA-MAD的近海光无线融合接入网络部署算法[J]. 光通信研究，2025(4): 250076. DOI： 10.13756/j.gtxyj.2025.250076.
Li X H, Xi T, Wang X, et al. Offshore Optical-wireless Integrated Access Network Deployment Algorithm based on MHA-MAD[J]. Study on Optical Communications, 2025(4): 250076. DOI： 10.13756/j.gtxyj.2025.250076.
摘要
【目的】
2
随着近海区域业务量的快速增长，带宽需求呈现出指数级增长趋势，承载业务的第五代移动通信技术（5G）及后5G（B5G）下一代无线接入网络（NG-RAN）资源即将枯竭，波分复用无源光网络（WDM-PON）因其高带宽等优势，成为支持5G/B5G NG-RAN的有效承载方案。然而，近海复杂多变的环境为WDM-PON的网络部署带来了严峻挑战，如高昂的部署成本、大规模的路径损耗以及恶劣的水下环境等，亟需通过优化网络部署策略降低成本、风险和传输损耗等，以构建适应近海环境的网络。
【方法】
2
文章提出了一种多头注意力增强的多智能体深度Q网络（MHA-MAD）算法，通过多头注意力机制高效提取网络环境的关键特征，并为不同特征赋予动态权重，从而提升建模精度。同时，采用多智能体框架，使多个智能体在共享网络环境中协作与同步决策，实现网络部署的全局优化。
【结果】
2
与现有基准方法相比，MHA-MAD算法在网络部署中使性能提高了近42%，其结果接近理论最优解。此外，与未利用多头注意力机制的多智能体深度Q网络（DQN）算法相比，MHA-MAD算法在最小化网络部署总成本、节点功耗、链路衰减和网络风险的联合优化目标上，性能提高了近8%。
【结论】
2
MHA-MAD算法为面向近海场景5G/B5G NG-RAN的WDM-PON部署与优化提供了新思路。
Abstract
【Objective】
2
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】
2
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】
2
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】
2
MHA-MAD provides new insights for the deployment and optimization of WDM-PON to support 5G/B5G NG-RAN in offshore scenarios.
关键词
Keywords
references
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