Deep Learning Models for Physical Layer Communications | Paper Citation Record

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This paper cites A mathematical theory of communication.The Bell System Technical Journal, 27(3):379–423, 7 1948.

Deep Learning Models for Physical Layer Communications A mathematical theory of communication.The Bell System Technical Journal, 27(3):379–423, 7 1948

Reference 1

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This paper cites Critique of Pure Reason.

Deep Learning Models for Physical Layer Communications Critique of Pure Reason

Reference 2

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Deep Learning Models for Physical Layer Communications Unresolved cited work

Reference 3

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This paper cites Deep learning.

Deep Learning Models for Physical Layer Communications Deep learning

Reference 4

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This paper cites A very brief introduction to machine learning with applications to communication systems.

Deep Learning Models for Physical Layer Communications A very brief introduction to machine learning with applications to communication systems

Reference 5

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This paper cites Deep learning for joint source- channel coding of text.

Deep Learning Models for Physical Layer Communications Deep learning for joint source- channel coding of text

Reference 6

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This paper cites Dorner, S.

Deep Learning Models for Physical Layer Communications Dorner, S

Reference 7

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This paper cites Deep learning based chan- nel estimation for massive mimo with mixed-resolution adcs.IEEE Communications Letters, 23(11):1989–1993, 2019.

Deep Learning Models for Physical Layer Communications Deep learning based chan- nel estimation for massive mimo with mixed-resolution adcs.IEEE Communications Letters, 23(11):1989–1993, 2019

Reference 8

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This paper cites Nachmani, E.

Deep Learning Models for Physical Layer Communications Nachmani, E

Reference 9

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This paper cites Deep learning-based channel estimation for beamspace mmwave massive mimo systems.IEEE Wireless Communications Letters, 7(5):852–855, 2018.

Deep Learning Models for Physical Layer Communications Deep learning-based channel estimation for beamspace mmwave massive mimo systems.IEEE Wireless Communications Letters, 7(5):852–855, 2018

Reference 10

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This paper cites O’Shea and J.

Deep Learning Models for Physical Layer Communications O’Shea and J

Reference 11

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This paper cites Deep learning-based csi feedback approach for time-varying massive mimo channels.IEEE Wireless Com- munications Letters, 8(2):416–419, 2019.

Deep Learning Models for Physical Layer Communications Deep learning-based csi feedback approach for time-varying massive mimo channels.IEEE Wireless Com- munications Letters, 8(2):416–419, 2019

Reference 12

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This paper cites Shah, Daniel J.

Deep Learning Models for Physical Layer Communications Shah, Daniel J

Reference 13

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This paper cites Channel agnostic end-to-end learning based communication systems with conditional gan.

Deep Learning Models for Physical Layer Communications Channel agnostic end-to-end learning based communication systems with conditional gan

Reference 14

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This paper cites Model-free training of end-to-end communica- tionsystems.

Deep Learning Models for Physical Layer Communications Model-free training of end-to-end communica- tionsystems

Reference 15

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This paper cites Letizia and Andrea M.

Deep Learning Models for Physical Layer Communications Letizia and Andrea M

Reference 16

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This paper cites Copula Density Neural Estimation.

Deep Learning Models for Physical Layer Communications Copula Density Neural Estimation

Reference 17

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Deep Learning Models for Physical Layer Communications Unresolved cited work

Reference 18

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Deep Learning Models for Physical Layer Communications Unresolved cited work

Reference 19

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This paper cites MIND: Maximum mutual information based neural decoder.IEEE Communications Letters, 26(12):2954–2958, 2022.

Deep Learning Models for Physical Layer Communications MIND: Maximum mutual information based neural decoder.IEEE Communications Letters, 26(12):2954–2958, 2022

Reference 20

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This paper cites Letizia and Andrea M.

Deep Learning Models for Physical Layer Communications Letizia and Andrea M

Reference 21

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This paper cites Mutual infor- mation estimation via f-divergence and data derangements.

Deep Learning Models for Physical Layer Communications Mutual infor- mation estimation via f-divergence and data derangements

Reference 22

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This paper cites Letizia, Andrea M.

Deep Learning Models for Physical Layer Communications Letizia, Andrea M

Reference 23

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Deep Learning Models for Physical Layer Communications Letizia and Andrea M

Reference 24

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This paper cites Righini, N.

Deep Learning Models for Physical Layer Communications Righini, N

Reference 25

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Deep Learning Models for Physical Layer Communications Letizia and Andrea M

Reference 26

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Deep Learning Models for Physical Layer Communications Letizia, Babak Salamat, and Andrea M

Reference 27

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Deep Learning Models for Physical Layer Communications Letizia, and Andrea M

Reference 28

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Deep Learning Models for Physical Layer Communications Mitchell.Machine Learning

Reference 29

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Deep Learning Models for Physical Layer Communications Sutton and Andrew G

Reference 30

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Deep Learning Models for Physical Layer Communications Arulkumaran, M

Reference 31

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Deep Learning Models for Physical Layer Communications Eldar, Andrea Goldsmith, D

Reference 32

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Deep Learning Models for Physical Layer Communications Deep Learning

Reference 33

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Deep Learning Models for Physical Layer Communications Unresolved cited work

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Deep Learning Models for Physical Layer Communications Kullback and R

Reference 35

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Deep Learning Models for Physical Layer Communications Hornik, M

Reference 36

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Deep Learning Models for Physical Layer Communications Unresolved cited work

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Deep Learning Models for Physical Layer Communications Lecun, L

Reference 39

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Deep Learning Models for Physical Layer Communications Unresolved cited work

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This paper cites A unified architecture for natural language processing: Deep neural networks with multitask learning.

Deep Learning Models for Physical Layer Communications A unified architecture for natural language processing: Deep neural networks with multitask learning

Reference 41

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Deep Learning Models for Physical Layer Communications Ex- tracting and composing robust features with denoising autoencoders

Reference 42

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Deep Learning Models for Physical Layer Communications What Regularized Auto-Encoders Learn from the Data Generating Distribution

Reference 43

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Deep Learning Models for Physical Layer Communications Con- tracting auto-encoders: Explicit invariance during feature extraction

Reference 44

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Deep Learning Models for Physical Layer Communications Kingma and Max Welling

Reference 45

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Deep Learning Models for Physical Layer Communications Denoising diffusion probabilistic models

Reference 46

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Deep Learning Models for Physical Layer Communications Understanding diffusion models: A unified perspective, 2022

Reference 47

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Deep Learning Models for Physical Layer Communications NICE: Non-linear Independent Components Estimation

Reference 48

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Deep Learning Models for Physical Layer Communications Kingma and Prafulla Dhariwal

Reference 49

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This paper cites Probabilistic non-linear principal component analysis with gaussian process latent variable models.Journal of Machine Learning Research, 6:1783–1816, 11 2005.

Deep Learning Models for Physical Layer Communications Probabilistic non-linear principal component analysis with gaussian process latent variable models.Journal of Machine Learning Research, 6:1783–1816, 11 2005

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Deep Learning Models for Physical Layer Communications Lawrence

Reference 51

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This paper cites Doesthewake-sleepalgorithm produce good density estimators? In Advances in Neural Information Processing Systems, NeurIPS, pages 661–667, 1995.

Deep Learning Models for Physical Layer Communications Doesthewake-sleepalgorithm produce good density estimators? In Advances in Neural Information Processing Systems, NeurIPS, pages 661–667, 1995

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Deep Learning Models for Physical Layer Communications Kschischang and Brendan J

Reference 53

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Deep Learning Models for Physical Layer Communications Deep autoregressive networks

Reference 54

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Deep Learning Models for Physical Layer Communications Conditional Image Generation with PixelCNN Decoders

Reference 55

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Deep Learning Models for Physical Layer Communications Attention is all you need

Reference 56

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Deep Learning Models for Physical Layer Communications Hinton and Simon Osindero

Reference 57

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Deep Learning Models for Physical Layer Communications Alain, Y

Reference 58

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Deep Learning Models for Physical Layer Communications Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C

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This paper cites Unsupervised representation learn- ing with deep convolutional generative adversarial networks.

Deep Learning Models for Physical Layer Communications Unsupervised representation learn- ing with deep convolutional generative adversarial networks

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Deep Learning Models for Physical Layer Communications A style-based generator architecture for generative adversarial networks

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Deep Learning Models for Physical Layer Communications f-gan: Training generative neural samplers using variational divergence minimization

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Deep Learning Models for Physical Layer Communications Cario and Barry L

Reference 63

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This paper cites A simple approach to the generation of uniformly distributed random variables with prescribed correlations.

Deep Learning Models for Physical Layer Communications A simple approach to the generation of uniformly distributed random variables with prescribed correlations

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Deep Learning Models for Physical Layer Communications Unresolved cited work

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This paper cites Fonctions de répartition à n dimensions et leurs marges.Publications de l’Institut de Statistique de l’Université de Paris, 8:229–231, 1959.

Deep Learning Models for Physical Layer Communications Fonctions de répartition à n dimensions et leurs marges.Publications de l’Institut de Statistique de l’Université de Paris, 8:229–231, 1959

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This paper cites Approximate uncertainty modeling in risk analysis with vine copulas.Risk analysis : an official publication of the Society for Risk Analysis, 36(4):792—815, April 2016.

Deep Learning Models for Physical Layer Communications Approximate uncertainty modeling in risk analysis with vine copulas.Risk analysis : an official publication of the Society for Risk Analysis, 36(4):792—815, April 2016

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Deep Learning Models for Physical Layer Communications Unresolved cited work

Reference 68

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This paper cites Probability density decomposition for conditionally dependent random variables modeled by vines.Ann.

Deep Learning Models for Physical Layer Communications Probability density decomposition for conditionally dependent random variables modeled by vines.Ann

Reference 69

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This paper cites Copulas as High-Dimensional Generative Models: Vine Copula Autoencoders.

Deep Learning Models for Physical Layer Communications Copulas as High-Dimensional Generative Models: Vine Copula Autoencoders

Reference 70

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Observation 22bc18a3-1f32-4b87-b156-2dcdc29bae39 · outbound

This paper cites Convergence de la répartition empirique vers la répartition théorique.

Deep Learning Models for Physical Layer Communications Convergence de la répartition empirique vers la répartition théorique

Reference 71

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This paper cites Borgwardt, Malte J.

Deep Learning Models for Physical Layer Communications Borgwardt, Malte J

Reference 72

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This paper cites Roy, and Zoubin Ghahramani.

Deep Learning Models for Physical Layer Communications Roy, and Zoubin Ghahramani

Reference 73

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This paper cites MNIST handwritten digit database.

Deep Learning Models for Physical Layer Communications MNIST handwritten digit database

Reference 74

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Deep Learning Models for Physical Layer Communications Deeplearningfaceattributes in the wild

Reference 75

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This paper cites TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems.

Deep Learning Models for Physical Layer Communications TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

Reference 76

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Deep Learning Models for Physical Layer Communications Unresolved cited work

Reference 77

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This paper cites Pros and Cons of GAN Evaluation Measures.

Deep Learning Models for Physical Layer Communications Pros and Cons of GAN Evaluation Measures

Reference 78

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This paper cites Improved Techniques for Training GANs.

Deep Learning Models for Physical Layer Communications Improved Techniques for Training GANs

Reference 79

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This paper cites Gans trained by a two time-scale update rule converge to a local nash equilibrium.

Deep Learning Models for Physical Layer Communications Gans trained by a two time-scale update rule converge to a local nash equilibrium

Reference 80

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This paper cites Sutherland, Michael Arbel, and Arthur Gretton.

Deep Learning Models for Physical Layer Communications Sutherland, Michael Arbel, and Arthur Gretton

Reference 81

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Deep Learning Models for Physical Layer Communications Rethinking the Inception Architecture for Computer Vision

Reference 82

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Deep Learning Models for Physical Layer Communications Unresolved cited work

Reference 83

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This paper cites Pixel recurrent neural networks.

Deep Learning Models for Physical Layer Communications Pixel recurrent neural networks

Reference 84

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This paper cites Density estimation using real NVP.

Deep Learning Models for Physical Layer Communications Density estimation using real NVP

Reference 85

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This paper cites Wainwright, and Michael I.

Deep Learning Models for Physical Layer Communications Wainwright, and Michael I

Reference 86

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Observation 7bbe915d-4efd-4d4b-8511-721abdf84a70 · outbound

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Deep Learning Models for Physical Layer Communications Unresolved cited work

Reference 87

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This paper cites Copula-based kernel dependency measures.Proceedings of the 29th International Conference on Machine Learning, ICML 2012, 1, 06 2012.

Deep Learning Models for Physical Layer Communications Copula-based kernel dependency measures.Proceedings of the 29th International Conference on Machine Learning, ICML 2012, 1, 06 2012

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.611460Z

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Observation ffe4d66b-bbcf-4be6-84cd-fb6ae217a02d · outbound

This paper cites Distilling intractable generative models.

Deep Learning Models for Physical Layer Communications Distilling intractable generative models

Reference 89

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This paper cites Variational inference with normalizing flows.

Deep Learning Models for Physical Layer Communications Variational inference with normalizing flows

Reference 90

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This paper cites Wasserstein generative ad- versarial networks.

Deep Learning Models for Physical Layer Communications Wasserstein generative ad- versarial networks

Reference 91

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Deep Learning Models for Physical Layer Communications Generative moment matching networks

Reference 92

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This paper cites O’Shea, Tamoghna Roy, Nathan West, and Benjamin C.

Deep Learning Models for Physical Layer Communications O’Shea, Tamoghna Roy, Nathan West, and Benjamin C

Reference 93

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Deep Learning Models for Physical Layer Communications Unresolved cited work

Reference 94

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Deep Learning Models for Physical Layer Communications Unresolved cited work

Reference 95

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Deep Learning Models for Physical Layer Communications High-resolution image synthesis with latent diffusion models

Reference 96

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Deep Learning Models for Physical Layer Communications Adversarial audio synthesis

Reference 97

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Deep Learning Models for Physical Layer Communications Griffin and Jae Lim

Reference 98

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This paper cites Progressive growing of GANs for improved quality, stability, and variation.

Deep Learning Models for Physical Layer Communications Progressive growing of GANs for improved quality, stability, and variation

Reference 99

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Deep Learning Models for Physical Layer Communications Unresolved cited work

Reference 100

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Deep Learning Models for Physical Layer Communications Mutual information neural estimation

Reference 101

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