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Approximating the Void: Learning Stochastic Channel Models from
  Observation with Variational Generative Adversarial Networks

Approximating the Void: Learning Stochastic Channel Models from Observation with Variational Generative Adversarial Networks

16 May 2018
Tim O'Shea
Tamoghna Roy
Nathan E. West
    GAN
ArXivPDFHTML

Papers citing "Approximating the Void: Learning Stochastic Channel Models from Observation with Variational Generative Adversarial Networks"

14 / 14 papers shown
Title
Building Semantic Communication System via Molecules: An End-to-End
  Training Approach
Building Semantic Communication System via Molecules: An End-to-End Training Approach
Yukun Cheng
Wei Chen
Bo Ai
31
3
0
15 Apr 2024
Diffusion Models for Accurate Channel Distribution Generation
Diffusion Models for Accurate Channel Distribution Generation
Muah Kim
Rick Fritschek
Rafael F. Schaefer
DiffM
36
8
0
19 Sep 2023
Sensing-Throughput Tradeoffs with Generative Adversarial Networks for
  NextG Spectrum Sharing
Sensing-Throughput Tradeoffs with Generative Adversarial Networks for NextG Spectrum Sharing
Yi Shi
Y. Sagduyu
33
1
0
27 Dec 2022
Semantics-Empowered Communication: A Tutorial-cum-Survey
Semantics-Empowered Communication: A Tutorial-cum-Survey
Zhilin Lu
Rongpeng Li
Kun Lu
Xianfu Chen
Ekram Hossain
Zhifeng Zhao
Honggang Zhang
51
19
0
16 Dec 2022
MIMO Channel Estimation using Score-Based Generative Models
MIMO Channel Estimation using Score-Based Generative Models
Marius Arvinte
Jonathan I. Tamir
DiffM
32
50
0
14 Apr 2022
Channel model for end-to-end learning of communications systems: A
  survey
Channel model for end-to-end learning of communications systems: A survey
Ijaz Ahmad
Seokjoo Shin
38
0
0
08 Apr 2022
End-to-End Autoencoder Communications with Optimized Interference
  Suppression
End-to-End Autoencoder Communications with Optimized Interference Suppression
Kemal Davaslioglu
T. Erpek
Y. Sagduyu
47
4
0
29 Dec 2021
Generative Adversarial Networks (GANs) in Networking: A Comprehensive
  Survey & Evaluation
Generative Adversarial Networks (GANs) in Networking: A Comprehensive Survey & Evaluation
Hojjat Navidan
P. Moshiri
M. Nabati
Reza Shahbazian
S. Ghorashi
V. Shah-Mansouri
David Windridge
15
84
0
10 May 2021
Physics-Based Deep Learning for Fiber-Optic Communication Systems
Physics-Based Deep Learning for Fiber-Optic Communication Systems
Christian Hager
H. Pfister
34
66
0
27 Oct 2020
Deep Learning for Wireless Communications
Deep Learning for Wireless Communications
T. Erpek
Tim O'Shea
Y. Sagduyu
Yi Shi
T. Clancy
36
135
0
12 May 2020
Unsupervised Linear and Nonlinear Channel Equalization and Decoding
  using Variational Autoencoders
Unsupervised Linear and Nonlinear Channel Equalization and Decoding using Variational Autoencoders
Avi Caciularu
D. Burshtein
25
48
0
21 May 2019
Generative Adversarial Network for Wireless Signal Spoofing
Generative Adversarial Network for Wireless Signal Spoofing
Yi Shi
Kemal Davaslioglu
Y. Sagduyu
GAN
AAML
25
78
0
03 May 2019
Model-free Training of End-to-end Communication Systems
Model-free Training of End-to-end Communication Systems
Fayçal Ait Aoudia
J. Hoydis
31
189
0
14 Dec 2018
An Introduction to Deep Learning for the Physical Layer
An Introduction to Deep Learning for the Physical Layer
Tim O'Shea
J. Hoydis
AI4CE
91
2,177
0
02 Feb 2017
1