Research Article
A Downlink Scheduling Algorithm Based on Network Slicing for 5G
@INPROCEEDINGS{10.1007/978-3-030-67720-6_15, author={Shanwei Wang and Bing Xi and Zhizhong Zhang and Bingguang Deng}, title={A Downlink Scheduling Algorithm Based on Network Slicing for 5G}, proceedings={Communications and Networking. 15th EAI International Conference, ChinaCom 2020, Shanghai, China, November 20-21, 2020, Proceedings}, proceedings_a={CHINACOM}, year={2021}, month={2}, keywords={5G Network-slicing S-MLWDF algorithm Resource scheduling Delay Resource blocks allocation}, doi={10.1007/978-3-030-67720-6_15} }- Shanwei Wang
Bing Xi
Zhizhong Zhang
Bingguang Deng
Year: 2021
A Downlink Scheduling Algorithm Based on Network Slicing for 5G
CHINACOM
Springer
DOI: 10.1007/978-3-030-67720-6_15
Abstract
Current cellular mobile network should satisfy the service requirements of the User Equipment (UE) applications through Radio Resource Management (RRM) mechanisms as much as possible. In order to improve the resource utilization rate and Quality of Experience (QoE) for downlink Real-Time (RT) services in 5G system. In this paper, based on the Modified Largest Weighted Delay First (M-LWDF) algorithm, a slicing-oriented resource scheduling algorithm-S-MLWDF is proposed with using 5G network slicing technology. S-MLWDF takes RB groups as the basic units of RA (resource allocation) and takes slices as the allocation object. During the process of in-slice scheduling, on account of the Channel Quality Indication (CQI) obtained from Base Station (BS) feedback and the allocation of RBs over time, the generated weighting factor can guarantee the edge users to get equal scheduling opportunities. Meanwhile, the modified queue delay and HARQ retransmission packets delay can solve the problem of surge in Packet Loss Rate (PLR) near the delay threshold. The simulated results show that the performance of the proposed algorithm is better than the traditional scheduling algorithms. Especially compared with M-LWDF, the fairness and PLR of S-MLWDF are optimized by about 10% and 16.3%, which can better meet the needs of users.
