Research Article
5G-Optimizing Network Coverage in Radio Self Organizing Networks by M/L Based Beam Tilt Algorithm
@INPROCEEDINGS{10.1007/978-3-319-33681-7_4, author={Premkumar Karthikeyan and Nagabushanam Hari Kumar and Srinivasan Aishwarya}, title={5G-Optimizing Network Coverage in Radio Self Organizing Networks by M/L Based Beam Tilt Algorithm}, proceedings={Smart City 360°. First EAI International Summit, Smart City 360°, Bratislava, Slovakia and Toronto, Canada, October 13-16, 2015. Revised Selected Papers}, proceedings_a={SMARTCITY360}, year={2016}, month={6}, keywords={eNodeB Azimuth angle SINR (Signal to Interference Noise Ratio) Long Term Evolution Self Optimizing Network CQI (Channel Quality Indicator)}, doi={10.1007/978-3-319-33681-7_4} }- Premkumar Karthikeyan
Nagabushanam Hari Kumar
Srinivasan Aishwarya
Year: 2016
5G-Optimizing Network Coverage in Radio Self Organizing Networks by M/L Based Beam Tilt Algorithm
SMARTCITY360
Springer
DOI: 10.1007/978-3-319-33681-7_4
Abstract
This paper proposes a novel machine learning based antenna beam tilt algorithm for minimizing the overall Poor Signal Strength (PSS) regions /dead zones in the network area considered. Our objective is to provide network intelligence and automation of the optimization of the configurable parameter, azimuth angle of the antenna, to adapt to varying channel conditions and rebalance the entire network so as to provide an optimized level of service to the users. The proposed scheme involves developing a simulation scenario for the existing network and employing machine learning to study the behavior of the network by taking large number of combinations of azimuth angles and corresponding measure of PSS area. Regression analysis and stochastic gradient descent are used to obtain the relationship and the optimized angles for which the PSS area is minimum. Our simulation results demonstrate the reduction in overall PSS area compared to state of art approaches.