An Effective and Efficient Dynamic eMBMS Multicast Grouping Scheduling Algorithm in MBSFNs for Public Safety Scenarios

Published

June 5, 2020

Author(s)

Siyuan Feng, Chunmei Liu, Chen Shen, HYEONG-AH Choi, Richard A. Rouil

Abstract

The LTE (Long-Term Evolution) eMBMS (evolved Multimedia Broadcast Multicast Service) technology employed in MBSFNs (Multicast Broadcast Single Frequency Networks) has been shown to be capable of considerably increasing the capacity of serving public safety users under group communication scenarios. However, due to its own limitations, more fine-tuned scheduling approach is needed in order to fully utilize the strengths of eMBMS multicast. In this work, we first identify and analyze several overlooked challenges for scheduling multicast traffic in MBSFNs. Then we develop an effective and efficient dynamic scheduling algorithm for eMBMS multicast in time and frequency varying channel. The proposed algorithm leverages the advantages of both multicast and unicast schemes via user grouping; and through extensive simulations, is shown to be capable of significantly enhancing the mission critical performances under both best effort and guaranteed bit rate delivery models. We also show the resiliency of our proposed algorithm by applying it onto various network and UE deployment scenarios. Our consistent results further prove that LTE eMBMS in MBSFN is a key solution in overcoming limitations in near future public safety networks.

Citation

IEEE Access Journal

Volume

8

Pub Type

Journals

Download Paper

https://doi.org/10.1109/ACCESS.2020.3000251

Local Download

Keywords

eMBMS, LTE, MBSFN, Mission Critical, Multicast, Public Safety, RAN, Scheduling

Citation

Feng, S. , Liu, C. , Shen, C. , Choi, H. and Rouil, R. (2020), An Effective and Efficient Dynamic eMBMS Multicast Grouping Scheduling Algorithm in MBSFNs for Public Safety Scenarios, IEEE Access Journal, [online], https://doi.org/10.1109/ACCESS.2020.3000251, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=930143 (Accessed December 8, 2024)