在新型人工智能驱动的云原生数据平台上使用长短期记忆模型进行近海开放无线接入网异常检测
Anomaly Detection in Offshore Open Radio Access Network Using Long Short-Term Memory Models on a Novel Artificial Intelligence-Driven Cloud-Native Data Platform
AI总结：
针对O-RAN部署挑战，Boldyn Networks开发了支持AI集成的云原生数据平台，并利用LSTM模型成功实现了近海风电场网络连接的异常检测。
摘要：
无线接入网（RAN）是现代电信基础设施的关键组成部分，目前正朝着解耦和开放架构演进。这些进步对于集成旨在通过引入认知能力来增强网络可靠性和运营自主性的智能数据驱动应用至关重要，正如新兴的开放无线接入网（O-RAN）标准所例证的那样。尽管具有潜力，但O-RAN技术的新生性质带来了挑战，主要是由于缺乏成熟的运营标准。这使得数据和智能应用的管理复杂化，特别是在与传统网络管理和运营支持系统集成时。不同的特定供应商设计方法进一步阻碍了迁移并限制了解决方案的可重用性。电信业务导向工程中的技能差距加剧了这些挑战，这仍然是有效部署O-RAN和开发智能应用的关键障碍。为了应对这些挑战，Boldyn Networks开发了一个新型云原生数据分析平台，专门设计用于支持O-RAN部署中可扩展的AI集成。该平台在真实场景中进行了严格测试，并应用了先进的人工智能（AI）技术以提高运营效率和客户体验。实施过程涉及采用开发运营（DevOps）实践，利用为AI应用量身定制的数据湖仓架构，并采用复杂的数据工程策略。该平台成功解决了现实近海风电场部署中固有的连接挑战，使用长短期记忆（LSTM）模型进行网络连接的异常检测。
英文摘要：
The Radio Access Network (RAN) is a critical component of modern telecommunications infrastructure, currently evolving towards disaggregated and open architectures. These advancements are pivotal for integrating intelligent, data-driven applications aimed at enhancing network reliability and operational autonomy through the introduction of cognitive capabilities, as exemplified by the emerging Open Radio Access Network (O-RAN) standards. Despite its potential, the nascent nature of O-RAN technology presents challenges, primarily due to the absence of mature operational standards. This complicates the management of data and intelligent applications, particularly when integrating with traditional network management and operational support systems. Divergent vendor-specific design approaches further hinder migration and limit solution reusability. These challenges are compounded by a skills gap in telecommunications business-oriented engineering, which remains a key barrier to effective O-RAN deployment and intelligent application development. To address these challenges, Boldyn Networks developed a novel cloud-native data analytics platform, specifically designed to support scalable AI integration within O-RAN deployments. This platform underwent rigorous testing in real-world scenarios, and applied advanced Artificial Intelligence (AI) techniques to improve operational efficiency and customer experience. Implementation involved adopting Development Operations (DevOps) practices, leveraging data lakehouse architectures tailored for AI applications, and employing sophisticated data engineering strategies. The platform successfully addresses connectivity challenges inherent in real-world offshore windfarm deployments using Long Short-Term Memory (LSTM) models for anomaly detection in network connectivity.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
