Machine learning for wireless communications in the Internet of Things: A comprehensive survey

J Jagannath, N Polosky, A Jagannath, F Restuccia… - Ad Hoc Networks, 2019 - Elsevier
Abstract The Internet of Things (IoT) is expected to require more effective and efficient
wireless communications than ever before. For this reason, techniques such as spectrum …

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 …

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 in physical layer communications: Evolution and prospects in 5G and 6G networks

C Mao, Z Mu, Q Liang, I Schizas, C Pan - IET Communications, 2023 - Wiley Online Library
With the rapid development of the communication industry in the fifth generation and the
advance towards the intelligent society of the sixth generation wireless networks, traditional …

DeepReceiver: A deep learning-based intelligent receiver for wireless communications in the physical layer

S Zheng, S Chen, X Yang - IEEE Transactions on Cognitive …, 2020 - ieeexplore.ieee.org
A canonical wireless communication system consists of a transmitter and a receiver. The
information bit stream is transmitted after coding, modulation, and pulse shaping. Due to the …

Deep neural networks for channel estimation in underwater acoustic OFDM systems

R Jiang, X Wang, S Cao, J Zhao, X Li - IEEE access, 2019 - ieeexplore.ieee.org
Orthogonal frequency division multiplexing (OFDM) provides a promising modulation
technique for underwater acoustic (UWA) communication systems. It is indispensable to …

Generative AI for physical layer communications: A survey

N Van Huynh, J Wang, H Du, DT Hoang… - IEEE Transactions …, 2024 - ieeexplore.ieee.org
The recent evolution of generative artificial intelligence (GAI) leads to the emergence of
groundbreaking applications such as ChatGPT, which not only enhances the efficiency of …

Deep learning based underwater acoustic OFDM communications

Y Zhang, J Li, Y Zakharov, X Li, J Li - Applied Acoustics, 2019 - Elsevier
In this paper, we present a deep learning based underwater acoustic (UWA) orthogonal
frequency-division multiplexing (OFDM) communication system. Unlike the traditional …

Joint neural network equalizer and decoder

W Xu, Z Zhong, Y Be'ery, X You… - 2018 15th International …, 2018 - ieeexplore.ieee.org
Recently, deep learning methods have shown significant improvements in communication
systems. In this paper, we study the equalization problem over the nonlinear channel using …

Unsupervised linear and nonlinear channel equalization and decoding using variational autoencoders

A Caciularu, D Burshtein - IEEE Transactions on Cognitive …, 2020 - ieeexplore.ieee.org
A new approach for blind channel equalization and decoding, variational inference, and
variational autoencoders (VAEs) in particular, is introduced. We first consider the …