Deep learning for physical-layer 5G wireless techniques: Opportunities, challenges and solutions

H Huang, S Guo, G Gui, Z Yang, J Zhang… - IEEE Wireless …, 2019 - ieeexplore.ieee.org
The new demands for high-reliability and ultra-high capacity wireless communication have
led to extensive research into 5G communications. However, current communication …

Deep-learning-enhanced NOMA transceiver design for massive MTC: Challenges, state of the art, and future directions

N Ye, J An, J Yu - IEEE Wireless Communications, 2021 - ieeexplore.ieee.org
Non-orthogonal multiple access (NOMA) is a promising evolution path to meet the
requirements of massive machine type communications (mMTC) in 5G and beyond …

The role of deep learning in NOMA for 5G and beyond communications

MK Hasan, M Shahjalal, MM Islam… - … in Information and …, 2020 - ieeexplore.ieee.org
In the coming future, it is obvious that the wireless networks will be congested with massive
amounts of data traffic with the increasing number of users. Current multiple access …

A tutorial on extremely large-scale MIMO for 6G: Fundamentals, signal processing, and applications

Z Wang, J Zhang, H Du, D Niyato, S Cui… - … Surveys & Tutorials, 2024 - ieeexplore.ieee.org
Extremely large-scale multiple-input-multiple-output (XL-MIMO), which offers vast spatial
degrees of freedom, has emerged as a potentially pivotal enabling technology for the sixth …

Deep learning-aided 6G wireless networks: A comprehensive survey of revolutionary PHY architectures

B Ozpoyraz, AT Dogukan, Y Gevez… - IEEE Open Journal …, 2022 - ieeexplore.ieee.org
Deep learning (DL) has proven its unprecedented success in diverse fields such as
computer vision, natural language processing, and speech recognition by its strong …

Deep learning for wireless physical layer: Opportunities and challenges

T Wang, CK Wen, H Wang, F Gao… - China …, 2017 - ieeexplore.ieee.org
Machine learning (ML) has been widely applied to the upper layers of wireless
communication systems for various purposes, such as deployment of cognitive radio and …

Transformer-empowered 6G intelligent networks: From massive MIMO processing to semantic communication

Y Wang, Z Gao, D Zheng, S Chen… - IEEE Wireless …, 2022 - ieeexplore.ieee.org
It is anticipated that 6G wireless networks will accelerate the convergence of the physical
and cyber worlds and enable a paradigm-shift in the way we deploy and exploit …

DeepNOMA: A unified framework for NOMA using deep multi-task learning

N Ye, X Li, H Yu, L Zhao, W Liu… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Non-orthogonal multiple access (NOMA) will provide massive connectivity for future Internet
of Things. However, the intrinsic non-orthogonality in NOMA makes it non-trivial to approach …

Deep learning for wireless communications: An emerging interdisciplinary paradigm

L Dai, R Jiao, F Adachi, HV Poor… - IEEE Wireless …, 2020 - ieeexplore.ieee.org
Wireless communications are envisioned to bring about dramatic changes in the future, with
a variety of emerging applications, such as virtual reality, Internet of Things, and so on …

[PDF][PDF] Key techniques for 5G wireless communications: network architecture, physical layer, and MAC layer perspectives

Z Ma, ZQ Zhang, ZG Ding… - Science CHINA …, 2015 - personalpages.manchester.ac.uk
The fourth generation (4G) mobile communication systems are offering service worldwide
steadily. Although 4G systems could be loaded with much more services and data than …