Machine Learning for Service Migration: A Survey

N Toumi, M Bagaa, A Ksentini - IEEE Communications Surveys …, 2023 - ieeexplore.ieee.org
Future communication networks are envisioned to satisfy increasingly granular and dynamic
requirements to accommodate the application and user demands. Indeed, novel immersive …

Energy-efficient resource allocation for heterogeneous edge-cloud computing

W Hua, P Liu, L Huang - IEEE Internet of Things Journal, 2023 - ieeexplore.ieee.org
With the rapid development of Internet of Things (IoT) technology, billions of mobile devices
(MDs) are putting a massive burden on limited radio resources. Mobile-edge computing …

Intelligent handover decision scheme using double deep reinforcement learning

MS Mollel, AI Abubakar, M Ozturk, S Kaijage… - Physical …, 2020 - Elsevier
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 …

[HTML][HTML] Knowledge-defined networking: Applications, challenges and future work

S Ashtari, I Zhou, M Abolhasan, N Shariati, J Lipman… - Array, 2022 - Elsevier
Future 6G wireless communication systems are expected to feature intelligence and
automation. Knowledge-defined networking (KDN) is an evolutionary step toward …

Paving the way toward mobile IAB: Problems, solutions and challenges

VF Monteiro, FRM Lima, DC Moreira… - IEEE Open Journal …, 2022 - ieeexplore.ieee.org
Deploying access and backhaul as wireless links, aka integrated access and backhaul
(IAB), is envisioned as a viable approach to enable flexible and dense networks. Even …

A survey on the handover management in 5G-NR cellular networks: aspects, approaches and challenges

A Haghrah, MP Abdollahi, H Azarhava… - EURASIP Journal on …, 2023 - Springer
With the purpose of providing higher data rate and ultra-reliable and low-latency
communications for the users, employing the small cells in the upcoming Fifth-Generation …

A survey on data analysis on large-Scale wireless networks: online stream processing, trends, and challenges

DSV Medeiros, HN Cunha Neto, MA Lopez… - Journal of Internet …, 2020 - Springer
In this paper we focus on knowledge extraction from large-scale wireless networks through
stream processing. We present the primary methods for sampling, data collection, and …

What machine learning predictor performs best for mobility prediction in cellular networks?

H Gebrie, H Farooq, A Imran - 2019 IEEE International …, 2019 - ieeexplore.ieee.org
It is envisaged that the future cellular networks (5G) will be able to meet the promising
capacity and quality of experience requirements through extreme network densification and …

Learning-based handover in mobile millimeter-wave networks

S Khosravi, H Shokri-Ghadikolaei… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Millimeter-wave (mmWave) communication is considered as a key enabler of ultra-high data
rates in the future cellular and wireless networks. The need for directional communication …

Vehicle trajectory prediction based on LSTM recurrent neural networks

A Ip, L Irio, R Oliveira - 2021 IEEE 93rd Vehicular Technology …, 2021 - ieeexplore.ieee.org
This work presents an effective tool to predict the future trajectories of vehicles when its
current and previous locations are known. We propose a Long Short-Term Memory (LSTM) …