Research Article
Mobile Load Balancing for 5G RAN Slicing in Mobile Network
@INPROCEEDINGS{10.1007/978-3-030-90196-7_21, author={Hong Xu and Liushan Zhou and Hong Shen and Tiankui Zhang}, title={Mobile Load Balancing for 5G RAN Slicing in Mobile Network}, proceedings={Artificial Intelligence for Communications and Networks. Third EAI International Conference, AICON 2021, Xining, China, October 23--24, 2021, Proceedings, Part I}, proceedings_a={AICON}, year={2021}, month={11}, keywords={RAN Network slicing SLA Mobile load balancing}, doi={10.1007/978-3-030-90196-7_21} }- Hong Xu
Liushan Zhou
Hong Shen
Tiankui Zhang
Year: 2021
Mobile Load Balancing for 5G RAN Slicing in Mobile Network
AICON
Springer
DOI: 10.1007/978-3-030-90196-7_21
Abstract
With the rapid development of mobile Internet, network slicing is defined as one of key technologies to deal with the issue of differentiated requirements of diversified services. The introduction of network slicing brings many challenges to the implementation of Radio Access Network (RAN). Considering Mobile Load Balancing (MLB) for RAN slicing, a mobile load balancing algorithm based on Deep Reinforcement Learning (DRL) is proposed. First of all, we propose a system utility model to measure the satisfaction of the system. And a mobile load balancing strategy for RAN slicing is proposed, including slice-level load control realized by adjusting the proportion of radio resource allocated to slice by Radio Remote Unit (RRU), and cell-level load balancing based on handoff. In order to improve the system satisfaction and maximize the system utility, the joint optimization problem of system satisfaction and utility function is proposed. The DRL is used to solve these optimization problems. The simulation results show that the proposed algorithm can effectively reduce the total number of handoffs in the system and bring less balancing overhead. In addition, the proposed algorithm can effectively reduce the number of unsatisfied users and achieve higher system satisfaction.
