Resource Reservation in Sliced Networks: An Explainable Artificial
Intelligence (XAI) Approach
Abstract
The growing complexity of wireless networks has sparked an upsurge in
the use of artificial intelligence (AI) within the telecommunication
industry in recent years. In network slicing, a key component of 5G that
enables network operators to lease their resources to third-party
tenants, AI models may be employed in complex tasks, such as short-term
resource reservation (STRR). When AI is used to make complex resource
management decisions with financial and service quality implications, it
is important that these decisions be understood by a human-in-the-loop.
In this paper, we apply state-of-the art techniques from the field of
Explainable AI (XAI) to the problem of STRR. Using real-world data to
develop an AI model for STRR, we demonstrate how our XAI methodology can
be used to explain the real-time decisions of the model, to reveal
trends about the model’s general behaviour, as well as aid in the
diagnosis of potential faults during the model’s development. In
addition, we quantitatively validate the faithfulness of the
explanations across an extensive range of XAI metrics to ensure they
remain trustworthy and actionable.