Machine learning-based handover failure prediction model for handover success rate improvement in 5g
This paper presents and evaluates a simple but effective approach for substantially reducing
inter-frequency handover (HO) failure rate. We build a machine learning model to forecast …
inter-frequency handover (HO) failure rate. We build a machine learning model to forecast …
A data-driven framework for inter-frequency handover failure prediction and mitigation
With 5G already deployed, challenges related to handover exacerbate due to the dense
base station deployment operating on a motley of frequencies. In this paper, we present and …
base station deployment operating on a motley of frequencies. In this paper, we present and …
Novel algorithm to reduce handover failure rate in 5G networks
V Mishra, D Das, NN Singh - 2020 IEEE 3rd 5G World Forum …, 2020 - ieeexplore.ieee.org
Ultra-reliable low latency communication (URLLC) is a key feature in 5G which requires
improved mobility performance and reliability. In future, the number of devices are going to …
improved mobility performance and reliability. In future, the number of devices are going to …
Deep-mobility: A deep learning approach for an efficient and reliable 5g handover
RA Paropkari, A Thantharate… - … Conference on Wireless …, 2022 - ieeexplore.ieee.org
5G cellular networks are being deployed all over the world and this architecture supports
ultra-dense network (UDN) deployment. Small cells have a very important role in providing …
ultra-dense network (UDN) deployment. Small cells have a very important role in providing …
PBPHS: a profile-based predictive handover strategy for 5G networks
J Sun, Y Zhang, M Trik - Cybernetics and Systems, 2024 - Taylor & Francis
One of the necessities of mobile networks is uninterrupted access to wireless services,
taking into account the requirements of quality of service. With the development of the fifth …
taking into account the requirements of quality of service. With the development of the fifth …
Intelligent handover decision scheme using double deep reinforcement learning
Handovers (HOs) have been envisioned to be more challenging in 5G networks due to the
inclusion of millimetre wave (mm-wave) frequencies, resulting in more intense base station …
inclusion of millimetre wave (mm-wave) frequencies, resulting in more intense base station …
Machine learning aided holistic handover optimization for emerging networks
In the wake of network densification and multi-band operation in emerging cellular networks,
mobility and handover management is becoming a major bottleneck. The problem is further …
mobility and handover management is becoming a major bottleneck. The problem is further …
Novel technique in 4G Handover parameter tuning and prediction using statistical trend analysis and supervised machine learning
T Elmahdy, AF Bendary - 2021 International Symposium on …, 2021 - ieeexplore.ieee.org
This paper displays a novel technique that allows tuning and predicting better values for A3
offset handover parameter for both inter-frequency and intra-frequency. The proposed work …
offset handover parameter for both inter-frequency and intra-frequency. The proposed work …
Effectiveness of handover control parameters on handover performance in 5G and beyond mobile networks
Mobility management is essential in mobile communication networks to provide a smooth
connection during users' mobility. Handover control parameters (HCPs), such as handover …
connection during users' mobility. Handover control parameters (HCPs), such as handover …
Deep Learning-Based Handover Prediction for 5G and Beyond Networks
JPSH Lima, ÁAM de Medeiros… - ICC 2023-IEEE …, 2023 - ieeexplore.ieee.org
Although the 5G New Radio standard empowers the mobile communication networks with
diverse technologies such as Massive MIMO, mmWave deployments, and much more, some …
diverse technologies such as Massive MIMO, mmWave deployments, and much more, some …