标题： 一种高效且自适应的框架用于实现水下高性能维护网络 显示英文标题

标题： An Efficient and Adaptive Framework for Achieving Underwater High-performance Maintenance Networks

摘要： 随着空间-空-地-水综合网络（SAGAIN）的发展，高速可靠网络服务可以在任何时间、任何地点获得。 然而，水下通信网络（UCN）的长传播延迟和有限的网络容量对SAGAIN的服务质量产生了负面影响。 为了解决这个问题，本文提出了U-HPNF，一个分层框架，旨在实现具有自我管理、自我配置和自我优化能力的高性能网络。 U-HPNF利用深度强化学习（DRL）的感知和决策能力来管理UCNs中的有限资源，包括通信带宽、计算资源和能源供应。 此外，我们引入联邦学习（FL）来迭代优化决策模型，从而减少通信开销并保护节点观测信息的隐私。 通过在智能汇聚层和聚合层部署数字孪生（DT），U-HPNF可以模拟多种网络场景并适应不同的网络QoS需求。 通过三层网络设计和两层DT，U-HPNF提供了一个原生人工智能的高性能水下网络。 数值结果表明，所提出的U-HPNF框架能够有效优化各种情况下的网络性能，并适应变化的QoS需求。 显示英文摘要

摘要： With the development of space-air-ground-aqua integrated networks (SAGAIN), high-speed and reliable network services are accessible at any time and any location. However, the long propagation delay and limited network capacity of underwater communication networks (UCN) negatively impact the service quality of SAGAIN. To address this issue, this paper presents U-HPNF, a hierarchical framework designed to achieve a high-performance network with self-management, self-configuration, and self-optimization capabilities. U-HPNF leverages the sensing and decision-making capabilities of deep reinforcement learning (DRL) to manage limited resources in UCNs, including communication bandwidth, computational resources, and energy supplies. Additionally, we incorporate federated learning (FL) to iteratively optimize the decision-making model, thereby reducing communication overhead and protecting the privacy of node observation information. By deploying digital twins (DT) at both the intelligent sink layer and aggregation layer, U-HPNF can mimic numerous network scenarios and adapt to varying network QoS requirements. Through a three-tier network design with two-levels DT, U-HPNF provides an AI-native high-performance underwater network. Numerical results demonstrate that the proposed U-HPNF framework can effectively optimize network performance across various situations and adapt to changing QoS requirements.