Research Article
Open RAN Deployment Using Advanced Radio Link Manager Framework to Support Mission Critical Services in 5G
@ARTICLE{10.4108/eai.15-3-2019.162140, author={Sree Lekshmi S and Seshaiah Ponnekanti}, title={Open RAN Deployment Using Advanced Radio Link Manager Framework to Support Mission Critical Services in 5G}, journal={EAI Endorsed Transactions on Cloud Systems}, volume={5}, number={14}, publisher={EAI}, journal_a={CS}, year={2019}, month={3}, keywords={5G networks, machine learning, radio link manager, scheduler, mission critical services}, doi={10.4108/eai.15-3-2019.162140} }- Sree Lekshmi S
Seshaiah Ponnekanti
Year: 2019
Open RAN Deployment Using Advanced Radio Link Manager Framework to Support Mission Critical Services in 5G
CS
EAI
DOI: 10.4108/eai.15-3-2019.162140
Abstract
Next generation networks or 5G will be “network of networks” that can support ultra-reliable and low latency communication, high data rate, huge connectivity and high security. Network transformation stirring towards virtualized Radio Access Network (v-RAN) and intelligent resource management are foreseen as key solutions to realise such varied 5G requirements. Effective Radio Resource Management (RRM) is crucial for Mission Critical (MC) services to underpin communication between smartphone, massive machines and tiny sensor devices. The paper explores pioneering research related to architecture and intelligent RRM that helps Service Providers (SPs) to design reference framework of an advanced Radio Link Manager (RLM) enabled by Machine Learning (ML). One example optimization for commercial network/Long Term Evolution (LTE) and some preliminary results are analysed to understand the reference framework. The paper addresses the general reference architecture framework of advanced Radio Link Manager to support Mission Critical services in 5G. The paper also discusses about the ongoing standardisation activities and open source initiatives in 5G RAN.
Copyright © 2019 Sree Lekshmi S et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
