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Towards Addressing Training Data Scarcity Challenge in Emerging Radio Access Networks: A Survey and Framework by Qureshi, Haneya Naeem; Masood, Usama; Manalastas, Marvin; Zaidi, Syed Muhammad Asad; Farooq, Hasan; Forgeat, Julien; Bouton, Maxime; Bothe, Shruti; Karlsson, Per; Rizwan, Ali; Imran, Ali is a scholarly article available to read on EtoBox.

What is Towards Addressing Training Data Scarcity Challenge in Emerging Radio Access Networks: A Survey and Framework about?

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 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 AI solutions is limited by a fundamental challenge faced by cellular network research community: scarcity of training data. 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 to address training data scarcity for various data types. This is followed by a framework to address the training data scarcity. The 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 testbe

Author

Qureshi, Haneya Naeem; Masood, Usama; Manalastas, Marvin; Zaidi, Syed Muhammad Asad; Farooq, Hasan; Forgeat, Julien; Bouton, Maxime; Bothe, Shruti; Karlsson, Per; Rizwan, Ali; Imran, Ali

Published

2023

Language

EN