A machine learning based framework for KPI maximization in emerging networks using mobility parameters
J Shodamola, U Masood… - … Black Sea Conference …, 2020 - ieeexplore.ieee.org
Current LTE network is faced with a plethora of Configuration and Optimization Parameters
(COPs), both hard and soft, that are adjusted manually to manage the network and provide …
(COPs), both hard and soft, that are adjusted manually to manage the network and provide …
Mobility robustness optimization in enterprise LTE femtocells
V Buenestado, JM Ruiz-Aviles, M Toril… - 2013 IEEE 77th …, 2013 - ieeexplore.ieee.org
Mobility robustness optimization has been identified as an important use case of Self-
Organizing Network (SON). In this paper, a self-optimization algorithm for tuning handover …
Organizing Network (SON). In this paper, a self-optimization algorithm for tuning handover …
Mobility management in 5G and beyond: a novel smart handover with adaptive Time-to-trigger and hysteresis margin
R Karmakar, G Kaddoum… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
The 5th Generation (5G) New Radio (NR) and beyond technologies will support enhanced
mobile broadband, very low latency communications, and huge numbers of mobile devices …
mobile broadband, very low latency communications, and huge numbers of mobile devices …
Mobility robustness optimization in self-organizing LTE femtocell networks
Femtocell is a promising solution for enhancing the indoor coverage and capacity in
wireless networks. However, for the small size of femtocell and potentially frequent power …
wireless networks. However, for the small size of femtocell and potentially frequent power …
Machine Learning Empowered Emerging Wireless Networks in 6G: Recent Advancements, Challenges & Future Trends
The upcoming 6G networks are sixth-sense next-generation communication networks with
an ever-increasing demand for enhanced end-to-end (E2E) connectivity towards a …
an ever-increasing demand for enhanced end-to-end (E2E) connectivity towards a …
Context-aware mobility management in HetNets: A reinforcement learning approach
The use of small cell deployments in heterogeneous network (HetNet) environments is
expected to be a key feature of 4G networks and beyond, and essential for providing higher …
expected to be a key feature of 4G networks and beyond, and essential for providing higher …
Mobility-aware deep reinforcement learning with glimpse mobility prediction in edge computing
Mobile/multi-access edge computing (MEC) is therefore developed to support the upcoming
AI-aware mobile services, which require low latency and intensive computation resources at …
AI-aware mobile services, which require low latency and intensive computation resources at …
Next point-of-attachment selection based on long short term memory model in wireless networks
Existing mobility management systems in cellular networks are ill-equipped to support Ultra-
Reliable and Low Latency Communication (URLLC) requirement of next generation …
Reliable and Low Latency Communication (URLLC) requirement of next generation …
Reliability and mobility load balancing in next generation self-organized networks: using stochastic learning automata
A Mohajer, M Bavaghar, H Farrokhi - Wireless Personal Communications, 2020 - Springer
Self-organizing networking (SON) is an automation technology designed to make the
planning, configuration, management, optimization and healing of mobile radio access …
planning, configuration, management, optimization and healing of mobile radio access …
[PDF][PDF] A comparative study of machine learning-based load balancing in high-speed
With the rapid developments of fifth generation (5G) mobile communication networks in
recent years, different use cases can now significantly benefit from 5G networks. One such …
recent years, different use cases can now significantly benefit from 5G networks. One such …