Research Article
Improved Network Analytics with novel feedback quantities for Self-Optimized Networks
@ARTICLE{10.4108/eai.12-2-2019.156589, author={Roshni Chatterjee and Tushar Vrind}, title={Improved Network Analytics with novel feedback quantities for Self-Optimized Networks}, journal={EAI Endorsed Transactions on Cloud Systems}, volume={4}, number={13}, publisher={EAI}, journal_a={CS}, year={2018}, month={12}, keywords={SON, Network Analytics, MDT, UE Feedback}, doi={10.4108/eai.12-2-2019.156589} }- Roshni Chatterjee
Tushar Vrind
Year: 2018
Improved Network Analytics with novel feedback quantities for Self-Optimized Networks
CS
EAI
DOI: 10.4108/eai.12-2-2019.156589
Abstract
With the ever-increasing Capital Expenditure (CAPEX) required for newer technologies like New Radio (NR) to meet 5th Generation (5G) requirements, it is imperative for the operators to look at reduced Operation and Maintenance (O&M), as a way to optimize the Return on Investment (RoI) over several years. Self-Optimizing Networks (SON) are emerging as the key component for cellular operators, and it is a big game changer for reduction in O&M of the operators by automatically enhancing network performance, coverage and capacity. In this paper, we propose schemes to add new dimensions to SON by incorporating novel measurement quantities in the of Minimization of Drive Test (MDT) logging feedback, which serve to enhance the functionality of Network Analytics. The incorporation of the measurements for ‘battery drain rate’, ‘mobility state’ and ‘out of coverage cause’ in the feedback can deliver substantial gain over existing feedback parameters.
Copyright © 2018 Roshni Chatterjee 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.