Toward Addressing Training Data Scarcity Challenge in Emerging Radio Access Networks: A Survey and Framework
Toward Addressing Training Data Scarcity Challenge in Emerging Radio Access Networks: A Survey and Framework
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DOI：
10.1109/comst.2023.3271419
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发表时间：
2023-04
影响因子：
35.6
通讯作者：
H. Qureshi;Usama Masood;Marvin Manalastas;Syed Muhammad Asad Zaidi;H. Farooq;Julien Forgeat;Maxime Bouton;Shruti Bothe;P. Karlsson;A. Rizwan;A. Imran
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文献类型：
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作者：
H. Qureshi;Usama Masood;Marvin Manalastas;Syed Muhammad Asad Zaidi;H. Farooq;Julien Forgeat;Maxime Bouton;Shruti Bothe;P. Karlsson;A. Rizwan;A. Imran
蜂窝网络的未来取决于基于人工智能（AI）的自动化，特别是无线接入网络（RAN）的操作、优化和故障排除。为了实现这种零接触自动化，文献中提出了无数基于人工智能的解决方案，利用人工智能建模和优化网络行为，以实现零接触自动化目标。然而，为了可靠地工作，基于人工智能的自动化需要大量的训练数据。因此，所提出的人工智能解决方案的成功受到蜂窝网络研究界面临的一个基本挑战的限制：训练数据的稀缺性。在本文中，我们提出了一个广泛的回顾经典和新兴的技术，以解决这一挑战。我们首先确定RAN中的常见数据类型及其已知用例。然后，我们对文献中用于解决各种数据类型的训练数据稀缺性的技术进行了分类调查。接下来是一个解决训练数据稀缺性的框架。所提出的框架建立在可用信息和技术组合的基础上，包括插值、基于领域知识的、生成对抗神经网络、迁移学习、自动编码器、少镜头学习、模拟器和试验台。还提出了潜在的新技术来丰富蜂窝网络中的稀缺数据，例如通过矩阵补全理论和利用不同类型的网络几何形状和网络参数的基于领域知识的技术。此外，还介绍了最先进的模拟器和试验台的概述，以使读者了解当前和新兴的平台，以访问真实数据，以克服数据稀缺的挑战。对训练数据稀缺性解决技术的广泛调查与提出的框架相结合，为给定类型的数据选择合适的技术，可以帮助研究人员和网络运营商选择适当的方法来克服利用人工智能实现无线接入网络自动化的数据稀缺性挑战。
The future of cellular networks is contingent on artificial intelligence (AI) based automation, particularly for radio access network (RAN) operation, optimization, and troubleshooting. To achieve such zero-touch automation, a myriad of AI-based solutions are being proposed in literature to leverage AI for modeling and optimizing network behavior to achieve the zero-touch automation goal. However, to work reliably, AI based automation, requires a deluge of training data. Consequently, the success of the proposed AI solutions is limited by a fundamental challenge faced by cellular network research community: scarcity of the training data. In this paper, we present an extensive review of classic and emerging techniques to address this challenge. We first identify the common data types in RAN and their known use-cases. We then present a taxonomized survey of techniques used in literature to address training data scarcity for various data types. This is followed by a framework to address the training data scarcity. The proposed framework builds on available information and combination of techniques including interpolation, domain-knowledge based, generative adversarial neural networks, transfer learning, autoencoders, few-shot learning, simulators and testbeds. Potential new techniques to enrich scarce data in cellular networks are also proposed, such as by matrix completion theory, and domain knowledge-based techniques leveraging different types of network geometries and network parameters. In addition, an overview of state-of-the art simulators and testbeds is also presented to make readers aware of current and emerging platforms to access real data in order to overcome the data scarcity challenge. The extensive survey of training data scarcity addressing techniques combined with proposed framework to select a suitable technique for given type of data, can assist researchers and network operators in choosing the appropriate methods to overcome the data scarcity challenge in leveraging AI to radio access network automation.