Knowledge Transfer based Radio and Computation Resource Allocation for 5G RAN Slicing

TL;DR

This paper introduces a knowledge transfer based resource allocation method for 5G RAN slicing, improving efficiency and performance by leveraging prior knowledge to optimize radio and computation resource distribution.

Contribution

The paper proposes a novel KTRA method that incorporates knowledge transfer to enhance joint resource allocation in 5G RAN slicing, outperforming existing Q-learning approaches.

Findings

18.4% lower URLLC delay

30.1% higher eMBB throughput

faster convergence speed

Abstract

To implement network slicing in 5G, resource allocation is a key function to allocate limited network resources such as radio and computation resources to multiple slices. However, the joint resource allocation also leads to a higher complexity in the network management. In this work, we propose a knowledge transfer based resource allocation (KTRA) method to jointly allocate radio and computation resources for 5G RAN slicing. Compared with existing works, the main difference is that the proposed KTRA method has a knowledge transfer capability. It is designed to use the prior knowledge of similar tasks to improve performance of the target task, e.g., faster convergence speed or higher average reward. The proposed KTRA is compared with Qlearning based resource allocation (QLRA), and KTRA method presents a 18.4% lower URLLC delay and a 30.1% higher eMBB throughput as well as a faster convergence speed.