FA Aoudia, J Hoydis - 2018 52nd Asilomar Conference on …, 2018 - ieeexplore.ieee.org
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 …
End-to-end learning of communications systems is a fascinating novel concept that has so far only been validated by simulations for block-based transmissions. It allows learning of …
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 …
V Raj, S Kalyani - IEEE Communications Letters, 2018 - ieeexplore.ieee.org
Recent developments in applying deep learning techniques to train end-to-end communication systems have shown great promise in improving the overall performance of …
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 …
We extend the idea of end-to-end learning of communications systems through deep neural network (NN)-based autoencoders to orthogonal frequency division multiplexing (OFDM) …
W Yu, F Sohrabi, T Jiang - IEEE BITS the Information Theory …, 2022 - ieeexplore.ieee.org
Traditional communication system design has always been based on the paradigm of first establishing a mathematical model of the communication channel, then designing and …
T O'shea, J Hoydis - IEEE Transactions on Cognitive …, 2017 - ieeexplore.ieee.org
We present and discuss several novel applications of deep learning for the physical layer. By interpreting a communications system as an autoencoder, we develop a fundamental …
N Wu, X Wang, B Lin, K Zhang - IEEE Access, 2019 - ieeexplore.ieee.org
Deep learning has been applied in physical-layer communications systems in recent years and has demonstrated fascinating results that were comparable or even better than human …