Mobile-centric wireless networks offer users a diverse range of services and experiences. However, existing intelligent routing methods often struggle to make suitable routing decisions during dynamic network changes, significantly limiting transmission performance. This paper proposes a dynamic adaptive routing method based on Deep Reinforcement Learning (DAR-DRL) to effectively address these challenges. First, to accurately model network state information in complex and dynamically changing routing tasks, we introduce a link-aware graph learning model (LA-GNN) that efficiently senses network information of varying structures through a hierarchical aggregated message-passing neural network. Second, to ensure routing reliability in dynamic environments, we design a hop-by-hop routing strategy featuring a large acceptance domain and a reliability guarantee reward function. This mechanism adaptively avoids routing holes and loops across various network scenarios while enhancing the robustness of routing under dynamic conditions. Experimental results demonstrate that the proposed DAR-DRL method achieves the network routing task with shorter end-to-end delays, lower packet loss rates, and higher throughput compared to existing mainstream methods across common dynamic network scenarios, including cases with dynamic traffic variations, random link failures (small topology changes), and significant topology alterations.

Zheheng Rao

Yanyan Xu

Ye Yao

Weizhi Meng

Computer Communications

计算机和通信网络是信息社会的关键基础设施，具有很高的社会经济价值，因为它们有助于许多关键服务（从医疗保健到金融和交通）的正确运行。互联网是当今计算机通信基础设施的核心。这已将互联网从用于计算机之间数据传输的强大网络转变为全球性、内容丰富的通信和信息系统，其中内容越来越多地由用户生成，因此，需要下一代网络技术、架构和协议来克服传统互联网的局限性，增加新的能力和服务，未来的互联网应该是无处不在、安全、有弹性、更接近人类的。通信范式。COMPUTER COMMUNICATIONS 是一份经过同行评审的国际期刊，发表高质量的科学文章（理论和实践）和调查论文，涵盖未来计算机通信网络的所有方面（在所有层，除了物理层），关注互联网架构、协议、服务和应用程序的演变。

Computer and communication networks are critical infrastructures in the information society, holding significant socio-economic value as they support the proper functioning of many essential services, including healthcare, finance, and transportation. The Internet serves as the core of today's computer communication infrastructure, having evolved from a powerful network for data transmission between computers into a global, content-rich communication and information system. With an increasing amount of user-generated content, there is a pressing need for next-generation network technologies, architectures, and protocols to overcome the limitations of the traditional Internet and enhance new capabilities and services. The future Internet should be ubiquitous, secure, resilient, and more aligned with human communication paradigms. "Computer Communications" is a peer-reviewed international journal that publishes high-quality scientific articles (both theoretical and practical) and survey papers covering all aspects of future computer communication networks (at all layers except the physical layer), with a focus on the evolution of Internet architectures, protocols, services, and applications.

计算机和通信网络是信息社会的关键基础设施，具有重要的社会经济价值，因为它们支持许多关键服务的正常运行，包括医疗保健、金融和交通等。互联网是当今计算机通信基础设施的核心，已经从一个用于计算机之间数据传输的强大网络，转变为一个全球性的、内容丰富的通信和信息系统。用户生成的内容日益增多，因此需要下一代网络技术、架构和协议，以克服传统互联网的局限性，增强新的能力和服务。未来的互联网应当是无处不在、安全、具有弹性，并更贴近人类的通信范式。《Computer Communications》是一份经过同行评审的国际期刊，发表高质量的科学文章（包括理论和实践）以及调查论文，涵盖未来计算机通信网络的各个方面（除了物理层），重点关注互联网架构、协议、服务和应用程序的演变。

Category	Quartile
计算机科学	3
计算机科学, 计算机信息系统	3
计算机科学, 工程电子与电气	3
计算机科学, 电信学	3

DAR-DRL: A Dynamic Adaptive Routing Method based on Deep Reinforcement Learning

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