Knowledge Transfer and Reuse: A Case Study of AI-enabled Resource Management in RAN Slicing

TL;DR

This paper surveys AI techniques for resource management in 5G/6G network slicing and introduces a novel knowledge transfer scheme (AKRM) that enhances system performance through transfer learning in RAN slicing.

Contribution

It presents a comprehensive survey of AI methods for network slicing and proposes a new knowledge transfer scheme (AKRM) for improved resource management in RAN slicing.

Findings

AKRM improves resource allocation efficiency.

Transfer learning enhances system performance.

Knowledge reuse reduces training time.

Abstract

An efficient resource management scheme is critical to enable network slicing in 5G networks and in envisioned 6G networks, and artificial intelligence (AI) techniques offer promising solutions. Considering the rapidly emerging new machine learning techniques, such as graph learning, federated learning, and transfer learning, a timely survey is needed to provide an overview of resource management and network slicing techniques of AI-enabled wireless networks. This article provides such a survey along with an application of knowledge transfer in radio access network (RAN) slicing. In particular, we firs provide some background on resource management and network slicing, and review relevant state-of-the-art AI and machine learning (ML) techniques and their applications. Then, we introduce our AI-enabled knowledge transfer and reuse-based resource management (AKRM) scheme, where we apply transfer learning to improve system performance. Compared with most existing works, which focus on the training of standalone agents from scratch, the main difference of AKRM lies in its knowledge transfer and reuse capability between different tasks. Our paper aims to be a roadmap for researchers to use knowledge transfer schemes in AI-enabled wireless networks, and we provide a case study over the resource allocation problem in RAN slicing.