Deep learning in mobile and wireless networking: A survey

C Zhang, P Patras, H Haddadi - IEEE Communications surveys …, 2019 - ieeexplore.ieee.org
The rapid uptake of mobile devices and the rising popularity of mobile applications and
services pose unprecedented demands on mobile and wireless networking infrastructure …

Transfer reinforcement learning for 5G new radio mmWave networks

M Elsayed, M Erol-Kantarci… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
In this paper, we aim at interference mitigation in 5G millimeter-Wave (mm-Wave)
communications by employing beamforming and Non-Orthogonal Multiple Access (NOMA) …

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 …

Federated learning for wireless communications: Motivation, opportunities, and challenges

S Niknam, HS Dhillon, JH Reed - IEEE Communications …, 2020 - ieeexplore.ieee.org
There is a growing interest in the wireless communications community to complement the
traditional model-driven design approaches with data-driven machine learning (ML)-based …

Machine learning meets computation and communication control in evolving edge and cloud: Challenges and future perspective

TK Rodrigues, K Suto, H Nishiyama… - … Surveys & Tutorials, 2019 - ieeexplore.ieee.org
Mobile Edge Computing (MEC) is considered an essential future service for the
implementation of 5G networks and the Internet of Things, as it is the best method of …

Artificial intelligence enabled wireless networking for 5G and beyond: Recent advances and future challenges

CX Wang, M Di Renzo, S Stanczak… - IEEE Wireless …, 2020 - ieeexplore.ieee.org
5G wireless communication networks are currently being deployed, and B5G networks are
expected to be developed over the next decade. AI technologies and, in particular, ML have …

AI-enabled future wireless networks: Challenges, opportunities, and open issues

M Elsayed, M Erol-Kantarci - IEEE Vehicular Technology …, 2019 - ieeexplore.ieee.org
An expected plethora of demanding services and use cases mandates a revolutionary shift
in the way future wireless network resources are managed. Indeed, when application …

[HTML][HTML] Deep learning-driven wireless communication for edge-cloud computing: opportunities and challenges

H Wu, X Li, Y Deng - Journal of Cloud Computing, 2020 - Springer
Future wireless communications are becoming increasingly complex with different radio
access technologies, transmission backhauls, and network slices, and they play an …

Real-time radio technology and modulation classification via an LSTM auto-encoder

Z Ke, H Vikalo - IEEE Transactions on Wireless …, 2021 - ieeexplore.ieee.org
Identification of the type of communication technology and/or modulation scheme based on
detected radio signal are challenging problems encountered in a variety of applications …

Edge learning for B5G networks with distributed signal processing: Semantic communication, edge computing, and wireless sensing

W Xu, Z Yang, DWK Ng, M Levorato… - IEEE journal of …, 2023 - ieeexplore.ieee.org
To process and transfer large amounts of data in emerging wireless services, it has become
increasingly appealing to exploit distributed data communication and learning. Specifically …