Channel agnostic end-to-end learning based communication systems with conditional GAN
In this article, we use deep neural networks (DNNs) to develop an end-to-end wireless
communication system, in which DNNs are employed for all signal-related functionalities …
communication system, in which DNNs are employed for all signal-related functionalities …
End-to-end learning of communications systems without a channel model
The idea of end-to-end learning of communications systems through neural network (NN)-
based autoencoders has the shortcoming that it requires a differentiable channel model. We …
based autoencoders has the shortcoming that it requires a differentiable channel model. We …
Deep learning-based end-to-end wireless communication systems with conditional GANs as unknown channels
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 …
networks (DNNs), where DNNs are employed to perform several key functions, including …
Deep learning for channel coding via neural mutual information estimation
R Fritschek, RF Schaefer… - 2019 IEEE 20th …, 2019 - ieeexplore.ieee.org
End-to-end deep learning for communication systems, ie, systems whose encoder and
decoder are learned, has attracted significant interest recently, due to its performance which …
decoder are learned, has attracted significant interest recently, due to its performance which …
Deep learning-based channel estimation algorithm over time selective fading channels
The research about deep learning application for physical layer has been received much
attention in recent years. In this paper, we propose a Deep Learning (DL) based channel …
attention in recent years. In this paper, we propose a Deep Learning (DL) based channel …
[HTML][HTML] Toward intelligent wireless communications: Deep learning-based physical layer technologies
S Liu, T Wang, S Wang - Digital Communications and Networks, 2021 - Elsevier
Advanced technologies are required in future mobile wireless networks to support services
with highly diverse requirements in terms of high data rate and reliability, low latency, and …
with highly diverse requirements in terms of high data rate and reliability, low latency, and …
Deep learning for wireless physical layer: Opportunities and challenges
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 …
communication systems for various purposes, such as deployment of cognitive radio and …
Meta-learning to communicate: Fast end-to-end training for fading channels
When a channel model is available, learning how to communicate on fading noisy channels
can be formulated as the (unsupervised) training of an autoencoder consisting of the …
can be formulated as the (unsupervised) training of an autoencoder consisting of the …
Deepchannel: Wireless channel quality prediction using deep learning
Accurately modeling and predicting wireless channel quality variations is essential for a
number of networking applications such as scheduling and improved video streaming over …
number of networking applications such as scheduling and improved video streaming over …
Wideband channel estimation with a generative adversarial network
E Balevi, JG Andrews - IEEE Transactions on Wireless …, 2021 - ieeexplore.ieee.org
Communication at high carrier frequencies such as millimeter wave (mmWave) and
terahertz (THz) requires channel estimation for very large bandwidths at low SNR. Hence …
terahertz (THz) requires channel estimation for very large bandwidths at low SNR. Hence …