  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 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 network parameters and geometries. An overview of
state-of-the art simulators and testbeds is also presented to make readers
aware of current and emerging platforms for real data access. 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 appropriate methods to overcome
the data scarcity challenge in leveraging AI to radio access network
automation.
