… The self-optimization falls down on RRM which also consists of two functions: mobility
robustness optimization (MRO) and load balancing optimization (LBO) [14]. MRO manages HO …

… a machine learning−based mobility robustness optimization algorithm to optimize handover
parameters for seamless mobility … network optimization, especially handover optimization. …

… Today, deep learning and machine learning have been proposed as solutions to many
problems such as obtaining the optimal HO triggering point [55]. Handover is one of those …

… The self-optimization falls down on RRM which also consists of two functions: mobility
robustness optimization (MRO) and load balancing optimization (LBO) [14]. MRO manages HO …

… In this work, EN-MRO is implemented with the Deep Learning Toolbox in Matlab. With that
… Conclusions In this paper, a novel QoE-aware mobility robustness optimization scheme for …

… To compare the average performance of different methods, we use the idea of cross-validation
from machine learning. All data are separated as a training subset for constructing the …

… mobility service, we propose a deep reinforcement learning-based sliceaware mobility
robustness optimization … -slice service assurance by optimizing slice-specific handover parameters…

… While optimizing handovers, there is a trade off between the … on the applicability of machine
learning algorithms in SON for … different classes of machine learning algorithms - Regression …

… This paper introduces an Artificial Intelligence Multiple Linear … of artificial intelligence (AI)
and machine learning in 5G and 6G cellular networks offers an opportunity to enhance mobility …

… To overcome these limitations, we propose a deep reinforcement learning (DRL)-based
MRO scheme where parameter adjustment sets are evaluated by a multi-agent double deep Q-…