Towards 6G wireless communication networks: Vision, enabling technologies, and new paradigm shifts

X You, CX Wang, J Huang, X Gao, Z Zhang… - Science China …, 2021 - Springer
The fifth generation (5G) wireless communication networks are being deployed worldwide
from 2020 and more capabilities are in the process of being standardized, such as mass …

AI for 5G: research directions and paradigms

X You, C Zhang, X Tan, S Jin, H Wu - Science China Information Sciences, 2019 - Springer
Wireless communication technologies such as fifth generation mobile networks (5G) will not
only provide an increase of 1000 times in Internet traffic in the next decade but will also offer …

Deep learning-based end-to-end wireless communication systems with conditional GANs as unknown channels

H Ye, L Liang, GY Li, BH Juang - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
In this article, we develop an end-to-end wireless communication system using deep neural
networks (DNNs), where DNNs are employed to perform several key functions, including …

Deep unfolding for communications systems: A survey and some new directions

A Balatsoukas-Stimming… - 2019 IEEE International …, 2019 - ieeexplore.ieee.org
Deep unfolding is a method of growing popularity that fuses iterative optimization algorithms
with tools from neural networks to efficiently solve a range of tasks in machine learning …

Artificial intelligence for 5G and beyond 5G: Implementations, algorithms, and optimizations

C Zhang, YL Ueng, C Studer… - IEEE Journal on Emerging …, 2020 - ieeexplore.ieee.org
The communication industry is rapidly advancing towards 5G and beyond 5G (B5G) wireless
technologies in order to fulfill the ever-growing needs for higher data rates and improved …

Communication algorithms via deep learning

H Kim, Y Jiang, R Rana, S Kannan, S Oh… - arXiv preprint arXiv …, 2018 - arxiv.org
Coding theory is a central discipline underpinning wireline and wireless modems that are
the workhorses of the information age. Progress in coding theory is largely driven by …

Turbo autoencoder: Deep learning based channel codes for point-to-point communication channels

Y Jiang, H Kim, H Asnani, S Kannan… - Advances in neural …, 2019 - proceedings.neurips.cc
Designing codes that combat the noise in a communication medium has remained a
significant area of research in information theory as well as wireless communications …

Machine learning meets communication networks: Current trends and future challenges

I Ahmad, S Shahabuddin, H Malik, E Harjula… - IEEE …, 2020 - ieeexplore.ieee.org
The growing network density and unprecedented increase in network traffic, caused by the
massively expanding number of connected devices and online services, require intelligent …

[PDF][PDF] Deep Learning Based End-to-End Wireless Communication Systems Without Pilots.

H Ye, GY Li, BH Juang - IEEE Trans. Cogn. Commun. Netw., 2021 - ieeexplore.ieee.org
The recent development in machine learning, especially in deep neural networks (DNN),
has enabled learning-based end-to-end communication systems, where DNNs are …

Ko codes: inventing nonlinear encoding and decoding for reliable wireless communication via deep-learning

AV Makkuva, X Liu, MV Jamali… - International …, 2021 - proceedings.mlr.press
Landmark codes underpin reliable physical layer communication, eg, Reed-Muller, BCH,
Convolution, Turbo, LDPC, and Polar codes: each is a linear code and represents a …