A tutorial on ultrareliable and low-latency communications in 6G: Integrating domain knowledge into deep learning

C She, C Sun, Z Gu, Y Li, C Yang… - Proceedings of the …, 2021 - ieeexplore.ieee.org
As one of the key communication scenarios in the fifth-generation and also the sixth-
generation (6G) mobile communication networks, ultrareliable and low-latency …

Cellular traffic prediction with machine learning: A survey

W Jiang - Expert Systems with Applications, 2022 - Elsevier
Cellular networks are important for the success of modern communication systems, which
support billions of mobile users and devices. Powered by artificial intelligence techniques …

Deep learning at the mobile edge: Opportunities for 5G networks

M McClellan, C Cervelló-Pastor, S Sallent - Applied Sciences, 2020 - mdpi.com
Mobile edge computing (MEC) within 5G networks brings the power of cloud computing,
storage, and analysis closer to the end user. The increased speeds and reduced delay …

Dynamic VNF placement, resource allocation and traffic routing in 5G

M Golkarifard, CF Chiasserini, F Malandrino… - Computer Networks, 2021 - Elsevier
Abstract 5G networks are going to support a variety of vertical services, with a diverse set of
key performance indicators (KPIs), by using enabling technologies such as software-defined …

Real‐World Wireless Network Modeling and Optimization: From Model/Data‐Driven Perspective

Y Li, S Zhang, X Ren, J Zhu, J Huang… - Chinese Journal of …, 2022 - Wiley Online Library
With the rapid development of the fifthgeneration wireless communication systems, a
profound revolution in terms of transmission capacity, energy efficiency, reliability, latency …

Energy and resource efficiency by user traffic prediction and classification in cellular networks

A Azari, F Salehi, P Papapetrou… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
There is a lack of research on the analysis of per-user traffic in cellular networks, for deriving
and following traffic-aware network management. In fact, the legacy design approach, in …

Machine learning: A catalyst for THz wireless networks

AAA Boulogeorgos, E Yaqub, M Di Renzo… - Frontiers in …, 2021 - frontiersin.org
With the vision to transform the current wireless network into a cyber-physical intelligent
platform capable of supporting bandwidth-hungry and latency-constrained applications, both …

Anticipatory allocation of communication and computational resources at the edge using spatio-temporal dynamics of mobile users

A Rago, G Piro, G Boggia, P Dini - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Multi-access Edge Computing represents a key enabling technology for emerging mobile
networks. It offers intensive computational resources very close to the end-users, useful for …

A Survey on Deep Learning for Cellular Traffic Prediction

X Wang, Z Wang, K Yang, Z Song, C Bian… - Intelligent …, 2024 - spj.science.org
With the widespread deployment of 5G networks and the proliferation of mobile devices,
mobile network operators are confronted not only with massive data growth in mobile traffic …

Selection of efficient and accurate prediction algorithm for employing real time 5G data load prediction

P Shrivastava, S Patel - 2021 IEEE 6th International …, 2021 - ieeexplore.ieee.org
In smart cities applications (ie intelligent transport systems, traffic management) cellular
traffic load prediction is playing an essential role. The cellular data consumption can help to …