A flexible scheduling algorithm for the 5th-generation networks

doi:10.23919/ICN.2020.0017

2021, Vol. 2

Issue (2): 101-107 doi: 10.23919/ICN.2020.0017

Series On Data Driven Intelligence, Sustainability, And Systems

A flexible scheduling algorithm for the 5th-generation networks

Lanlan Li^*(

),Wentao Shao(

),Xin Zhou(

)

Purple Mountain Lab, Nanjing 210000, China
School of Information Science and Engineering, Southeast University, Nanjing 210000, China

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Abstract

At present, the 5th-Generation (5G) wireless mobile communication standard has been released. 5G networks efficiently support enhanced mobile broadband traffic, ultra-reliable low-latency communication traffic, and massive machine-type communication. However, a major challenge for 5G networks is to achieve effective Radio Resource Management (RRM) strategies and scheduling algorithms to meet quality of service requirements. The Proportional Fair (PF) algorithm is widely used in the existing 5G scheduling technology. In the PF algorithm, RRM assigns a priority to each user which is served by gNodeB. The existing metrics of priority mainly focus on the flow rate. The purpose of this study is to explore how to improve the throughput of 5G networks and propose new scheduling schemes. In this study, the package delay of the data flow is included in the metrics of priority. The Vienna 5G System-Level (SL) simulator is a MATLAB-based SL simulation platform which is used to facilitate the research and development of 5G and beyond mobile communications. This paper presents a new scheduling algorithm based on the analysis of different scheduling schemes for radio resources using the Vienna 5G SL simulator.

Key words： 5th-Generation (5G) radio resource management channel status information reporting scheduling schemes Vienna simulator

Received: 08 July 2020 Online: 19 August 2021

Corresponding Authors: Lanlan Li E-mail: lilanlan@pmlabs.com.cn;wentao1996@aliyun.com;xzhou1105@163.com

About author: Lanlan Li received the master degree in applied mathematics from Xinjiang University in 2008 and the PhD degree in electrical engineering from Southeast University in 2005. She is currently a senior engineering at Purple Mountain Lab. She has been involved in several national and entreprise’s projects. Her research interests lie in radio resource management, signal processing for digital communications, and AI application in communication field.|Wentao Shao received the BEng degree in electronic engineering from Chongqing University in 2018. Since September 2018, he has been pursuing the MS degree at School of Information Science and Engineering, Southeast University. His current research interests include stochastic modelling, wireless network optimization, firmware development, and network virtualization. His presentation of the work: An optimal estimation of base station density based on a new 5G transmission model in ICTC 2020 was selected as the best of the session.|Xin Zhou recieved the bachelor degree in information engineering from Nanjing University of Aeronautics and Astronautics, China in 2018. He is currently pursuing the master degree at Southeast University majoring in electronic and communication engineering. His research interests include communication channel modeling, radio resource scheduling, 5G system simulation, and application of reinforcement learning in communication field.


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Cite this article:

Lanlan Li,Wentao Shao,Xin Zhou. A flexible scheduling algorithm for the 5th-generation networks. , 2021, 2: 101-107.

URL:

http://icn.tsinghuajournals.com/10.23919/ICN.2020.0017 OR http://icn.tsinghuajournals.com/Y2021/V2/I2/101

Fig. 1 NSA architecture. Here S-GW represents Serving Gate W, EN represents EUTRA-NR, and X2 is the interface between two eNBs.

Fig. 2 SA architecture. Here, UPF represents user plane function and Xn is the interface between two gNBs.

Fig. 3 User plane protocol stack. Here SDAP represents Service Data Adaptation Protocol, PDCP represents Packet Data Convergence Protocol, and RLC represents Radio Link Control.

Fig. 4 Control plane protocol stack.

Fig. 5 CSI reporting. Here MCS represents Modulation and Coding Scheme and DL represents Downlink.

Table 1 Simulator parameters.

Fig. 6 Comparison of simulation throughput results of four scheduling algorithms. Here PF0 represents the existing Proportional Fair algorithm.


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