Meta-ViterbiNet: Online meta-learned Viterbi equalization for non-stationary channels
Deep neural networks (DNNs) based digital receivers can potentially operate in complex
environments. How-ever, the dynamic nature of communication channels implies that in …
environments. How-ever, the dynamic nature of communication channels implies that in …
Symbol-level online channel tracking for deep receivers
Y Cohen, T Raviv, N Shlezinger - ICASSP 2022-2022 IEEE …, 2022 - ieeexplore.ieee.org
Deep neural networks (DNNs) allow digital receivers to operate in complex environments by
learning from data corresponding to the channel input-output relationship. Since …
learning from data corresponding to the channel input-output relationship. Since …
ViterbiNet: A deep learning based Viterbi algorithm for symbol detection
Symbol detection plays an important role in the implementation of digital receivers. In this
work, we propose ViterbiNet, which is a data-driven symbol detector that does not require …
work, we propose ViterbiNet, which is a data-driven symbol detector that does not require …
On the Robustness of Deep Learning-aided Symbol Detectors to Varying Conditions and Imperfect Channel Knowledge
Recently, a data-driven Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm tailored to channels
with intersymbol interference has been introduced. This so-called BCJRNet algorithm …
with intersymbol interference has been introduced. This so-called BCJRNet algorithm …
ViterbiNet: Symbol detection using a deep learning based Viterbi algorithm
Symbol detection plays an important role in the implementation of digital receivers. One of
the most common symbol detection schemes is the Viterbi algorithm, which is capable of …
the most common symbol detection schemes is the Viterbi algorithm, which is capable of …
Mind: Model independent neural decoder
Standard decoding approaches rely on model-based channel estimation methods to
compensate for varying channel effects, which degrade in performance whenever there is a …
compensate for varying channel effects, which degrade in performance whenever there is a …
Autoencoder based robust transceivers for fading channels using deep neural networks
SR Mattu, TL Narasimhan… - 2020 IEEE 91st …, 2020 - ieeexplore.ieee.org
In this paper, we design transceivers for fading channels using autoencoders and deep
neural networks (DNN). Specifically, we consider the problem of finding (n, k) block codes …
neural networks (DNN). Specifically, we consider the problem of finding (n, k) block codes …
Generative-adversarial-network enabled signal detection for communication systems with unknown channel models
L Sun, Y Wang, AL Swindlehurst… - IEEE Journal on …, 2020 - ieeexplore.ieee.org
The Viterbi algorithm is widely adopted in digital communication systems because of its
capability of realizing maximum-likelihood signal sequence detection. However …
capability of realizing maximum-likelihood signal sequence detection. However …
Initial results on deep learning for joint channel equalization and decoding
Historically, most of the channel encoding and decoding algorithms have been designed to
deal with and evaluated under the additive white Gaussian noise (AWGN) channel …
deal with and evaluated under the additive white Gaussian noise (AWGN) channel …
Learning for detection: A deep learning wireless communication receiver over Rayleigh fading channels
A Al-Baidhani, HH Fan - 2019 International Conference on …, 2019 - ieeexplore.ieee.org
The evolution of data driven optimization has been shown advantageous in many
applications. In this paper, we propose a deep learning architecture for the wireless …
applications. In this paper, we propose a deep learning architecture for the wireless …