From 4G to 5G: Self-organized network management meets machine learning
J Moysen, L Giupponi - Computer Communications, 2018 - Elsevier
Self-organization as applied to cellular networks is usually referred to Selforganizing
Networks (SONs), and it is a key driver for improving Operations, Administration, and …
Networks (SONs), and it is a key driver for improving Operations, Administration, and …
Real‐World Wireless Network Modeling and Optimization: From Model/Data‐Driven Perspective
With the rapid development of the fifthgeneration wireless communication systems, a
profound revolution in terms of transmission capacity, energy efficiency, reliability, latency …
profound revolution in terms of transmission capacity, energy efficiency, reliability, latency …
Optimizing coverage and capacity in cellular networks using machine learning
RM Dreifuerst, S Daulton, Y Qian… - ICASSP 2021-2021 …, 2021 - ieeexplore.ieee.org
Wireless cellular networks have many parameters that are normally tuned upon deployment
and re-tuned as the network changes. Many operational parameters affect reference signal …
and re-tuned as the network changes. Many operational parameters affect reference signal …
An efficient stochastic gradient descent algorithm to maximize the coverage of cellular networks
Network coverage and capacity optimization is an important operational task in cellular
networks. The network coverage maximization by adjusting azimuths and tilts of antennas is …
networks. The network coverage maximization by adjusting azimuths and tilts of antennas is …
Remote electrical tilt optimization via safe reinforcement learning
Remote Electrical Tilt (RET) optimization is an efficient method for adjusting the vertical tilt
angle of Base Stations (BSs) antennas in order to optimize Key Performance Indicators …
angle of Base Stations (BSs) antennas in order to optimize Key Performance Indicators …
Online antenna tuning in heterogeneous cellular networks with deep reinforcement learning
E Balevi, JG Andrews - IEEE Transactions on Cognitive …, 2019 - ieeexplore.ieee.org
We aim to jointly optimize antenna tilt angle, and vertical and horizontal half-power
beamwidths of the macrocells in a heterogeneous cellular network (HetNet). The …
beamwidths of the macrocells in a heterogeneous cellular network (HetNet). The …
Concurrent optimization of coverage, capacity, and load balance in HetNets through soft and hard cell association parameters
Ultradense heterogeneous networks (HetNets) are emerging as an inevitable approach to
tackle the capacity crunch in cellular networks. However, imbalanced load among small and …
tackle the capacity crunch in cellular networks. However, imbalanced load among small and …
Learning optimal antenna tilt control policies: A contextual linear bandits approach
Controlling antenna tilts in cellular networks is critical to achieve a good trade-off between
network coverage and capacity. We devise algorithms learning optimal tilt control policies …
network coverage and capacity. We devise algorithms learning optimal tilt control policies …
A computationally efficient method for QoE-driven self-planning of antenna tilts in a LTE network
PAS Ordonez, S Luna-Ramirez, M Toril - IEEE Access, 2020 - ieeexplore.ieee.org
In future mobile communications systems, network management procedures must be
upgraded to consider user quality of experience (QoE) to deal with service diversity. In this …
upgraded to consider user quality of experience (QoE) to deal with service diversity. In this …
Deep learning based prediction of traffic peaks in mobile networks
In mobile networks, it is essential to configure networks more efficiently to provide mobile
users with services having better quality. For the adjacent cells, sometimes the mobile traffic …
users with services having better quality. For the adjacent cells, sometimes the mobile traffic …