Artificial neural networks-based machine learning for wireless networks: A tutorial

M Chen, U Challita, W Saad, C Yin… - … Surveys & Tutorials, 2019 - ieeexplore.ieee.org
In order to effectively provide ultra reliable low latency communications and pervasive
connectivity for Internet of Things (IoT) devices, next-generation wireless networks can …

5G backhaul challenges and emerging research directions: A survey

M Jaber, MA Imran, R Tafazolli, A Tukmanov - IEEE access, 2016 - ieeexplore.ieee.org
5G is the next cellular generation and is expected to quench the growing thirst for taxing
data rates and to enable the Internet of Things. Focused research and standardization work …

Machine learning for 5G/B5G mobile and wireless communications: Potential, limitations, and future directions

ME Morocho-Cayamcela, H Lee, W Lim - IEEE access, 2019 - ieeexplore.ieee.org
Driven by the demand to accommodate today's growing mobile traffic, 5G is designed to be
a key enabler and a leading infrastructure provider in the information and communication …

Ultrareliable and low-latency wireless communication: Tail, risk, and scale

M Bennis, M Debbah, HV Poor - Proceedings of the IEEE, 2018 - ieeexplore.ieee.org
Ensuring ultrareliable and low-latency communication (URLLC) for 5G wireless networks
and beyond is of capital importance and is currently receiving tremendous attention in …

Comparison of edge computing implementations: Fog computing, cloudlet and mobile edge computing

K Dolui, SK Datta - 2017 Global Internet of Things Summit …, 2017 - ieeexplore.ieee.org
When it comes to storage and computation of large scales of data, Cloud Computing has
acted as the de-facto solution over the past decade. However, with the massive growth in …

Caching in the sky: Proactive deployment of cache-enabled unmanned aerial vehicles for optimized quality-of-experience

M Chen, M Mozaffari, W Saad, C Yin… - IEEE Journal on …, 2017 - ieeexplore.ieee.org
In this paper, the problem of proactive deployment of cache-enabled unmanned aerial
vehicles (UAVs) for optimizing the quality-of-experience (QoE) of wireless devices in a cloud …

Optimal UAV caching and trajectory in aerial-assisted vehicular networks: A learning-based approach

H Wu, F Lyu, C Zhou, J Chen, L Wang… - IEEE Journal on …, 2020 - ieeexplore.ieee.org
In this article, we investigate the UAV-aided edge caching to assist terrestrial vehicular
networks in delivering high-bandwidth content files. Aiming at maximizing the overall …

The role of caching in future communication systems and networks

GS Paschos, G Iosifidis, M Tao… - IEEE Journal on …, 2018 - ieeexplore.ieee.org
This paper has the following ambitious goal: to convince the reader that content caching is
an exciting research topic for the future communication systems and networks. Caching has …

[PDF][PDF] Machine learning for wireless networks with artificial intelligence: A tutorial on neural networks

M Chen, U Challita, W Saad, C Yin… - arXiv preprint arXiv …, 2017 - researchgate.net
Next-generation wireless networks must support ultra-reliable, low-latency communication
and intelligently manage a massive number of Internet of Things (IoT) devices in real-time …

Joint communication, computation, caching, and control in big data multi-access edge computing

A Ndikumana, NH Tran, TM Ho, Z Han… - IEEE Transactions …, 2019 - ieeexplore.ieee.org
The concept of Multi-access Edge Computing (MEC) has been recently introduced to
supplement cloud computing by deploying MEC servers to the network edge so as to reduce …