Deep Learning Models for Physical Layer Communications | Paper Citation Record

Observation 02d488fa-5fc4-4bca-912d-e1454ecba0e2 · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.182602Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 624e1a8f-6b46-4e05-9d4f-145bbb773a21 · outbound

This paper cites Critique of Pure Reason.

Deep Learning Models for Physical Layer Communications Critique of Pure Reason

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.189343Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dd85ee57-88f5-45e6-bc2e-bdadc1124369 · outbound

This paper cites an unresolved cited work.

Deep Learning Models for Physical Layer Communications Unresolved cited work

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.194939Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9225a035-cab1-4548-9844-bf71e24b3da9 · outbound

This paper cites Deep learning.

Deep Learning Models for Physical Layer Communications Deep learning

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.200195Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 35de20ee-41df-45dc-bc6f-d4cdcdb812f1 · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.205025Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac06955e-e421-45ba-9aa3-f2f1f489a626 · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.209482Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3079d2e5-f431-427c-bdb0-6cab487e437b · outbound

This paper cites Dorner, S.

Deep Learning Models for Physical Layer Communications Dorner, S

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.214465Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 998dc03a-9c7a-446a-89c3-4257e78cf191 · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.219499Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa6df2b2-49a3-41f7-8695-772217b1d5d2 · outbound

This paper cites Nachmani, E.

Deep Learning Models for Physical Layer Communications Nachmani, E

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.224441Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7da68020-958d-47bc-b4f5-42cb4efaa492 · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.229160Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 315103e2-48b5-4594-a73d-0d2354dba430 · outbound

This paper cites O’Shea and J.

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

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.234209Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d901d82-960a-4693-916b-59b09f91d485 · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.238274Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 367e45e7-a9b3-4888-9a98-1b45f830b46f · outbound

This paper cites Shah, Daniel J.

Deep Learning Models for Physical Layer Communications Shah, Daniel J

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.243409Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1af1662-a038-4b85-890c-0a3a759ddb66 · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.248389Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac751f91-9f72-4122-80fc-6e39800e914a · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.253682Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 99b05907-f74b-4c34-9a0e-6ed0c31d94e2 · outbound

This paper cites Letizia and Andrea M.

Deep Learning Models for Physical Layer Communications Letizia and Andrea M

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.258219Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a1349afa-6ff3-49cf-8194-0cd3fb619e43 · outbound

This paper cites Copula Density Neural Estimation.

Deep Learning Models for Physical Layer Communications Copula Density Neural Estimation

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-08-08T21:09:30.587436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e6ac6e87-e427-4825-9b63-623e9688db5b · outbound

This paper cites an unresolved cited work.

Deep Learning Models for Physical Layer Communications Unresolved cited work

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.269041Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b79a3cb9-71b6-4495-b42d-2e24b5c96fe4 · outbound

This paper cites an unresolved cited work.

Deep Learning Models for Physical Layer Communications Unresolved cited work

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.273455Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f2319ea0-791d-4e70-8bf8-227d4102797b · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.278225Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 74d70fec-34b5-4b4f-a60d-45f05c7e4f8c · outbound

This paper cites Letizia and Andrea M.

Deep Learning Models for Physical Layer Communications Letizia and Andrea M

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.282889Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 101d8d40-76e8-4694-8aac-6c1b8b5d6942 · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.287697Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 24b74df5-bc86-4103-b11f-3cc9583bc98f · outbound

This paper cites Letizia, Andrea M.

Deep Learning Models for Physical Layer Communications Letizia, Andrea M

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.292832Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da738289-13b9-4c2c-b4b0-7c31ceb337ef · outbound

This paper cites Letizia and Andrea M.

Deep Learning Models for Physical Layer Communications Letizia and Andrea M

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.297097Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b0684aa9-84f1-4214-ae50-049648bb7f77 · outbound

This paper cites Righini, N.

Deep Learning Models for Physical Layer Communications Righini, N

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.301794Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d8458c0f-1b8d-42c6-b1b6-a66e57aff606 · outbound

This paper cites Letizia and Andrea M.

Deep Learning Models for Physical Layer Communications Letizia and Andrea M

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.306559Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 29d93e5f-6ab6-4737-ad5f-f762c5db11a3 · outbound

This paper cites Letizia, Babak Salamat, and Andrea M.

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

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.312519Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bb072964-dcc3-4e26-b00d-efb3507186b5 · outbound

This paper cites Letizia, and Andrea M.

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

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.317155Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cf23fb9f-9f25-4d12-aa46-541805f71068 · outbound

This paper cites Mitchell.Machine Learning.

Deep Learning Models for Physical Layer Communications Mitchell.Machine Learning

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.321508Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6d4706a0-d136-4ea8-97e2-4a9066a5d571 · outbound

This paper cites Sutton and Andrew G.

Deep Learning Models for Physical Layer Communications Sutton and Andrew G

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.325691Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e05194bd-a914-40a9-b5cc-a22f27398d2b · outbound

This paper cites Arulkumaran, M.

Deep Learning Models for Physical Layer Communications Arulkumaran, M

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.329712Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e813427-970c-44da-97e5-05bf969a36c7 · outbound

This paper cites Eldar, Andrea Goldsmith, D.

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

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.333792Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fac981a9-6cd0-4f48-b4d0-a9354d37b419 · outbound

This paper cites Deep Learning.

Deep Learning Models for Physical Layer Communications Deep Learning

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.338799Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5d1b6149-455b-40f0-a0e4-88afb773acdd · outbound

This paper cites an unresolved cited work.

Deep Learning Models for Physical Layer Communications Unresolved cited work

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.344101Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5a0aaf95-a2b6-4b26-8dd6-f6c86ecc86d2 · outbound

This paper cites Kullback and R.

Deep Learning Models for Physical Layer Communications Kullback and R

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.348076Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1104287d-31bd-4441-962b-1ee2bd1ed7e5 · outbound

This paper cites Hornik, M.

Deep Learning Models for Physical Layer Communications Hornik, M

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.352843Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 64bc56ab-cefb-4d11-b961-ce0e1032339c · outbound

This paper cites an unresolved cited work.

Deep Learning Models for Physical Layer Communications Unresolved cited work

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.357238Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 99cf4082-ebfe-483f-a552-f354dbf243d3 · outbound

This paper cites Lecun, L.

Deep Learning Models for Physical Layer Communications Lecun, L

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.367087Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c79da369-1001-470d-af61-f8af194bf946 · outbound

This paper cites an unresolved cited work.

Deep Learning Models for Physical Layer Communications Unresolved cited work

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.371599Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 44e76bc5-34dc-490f-92bd-b0a3e9dcc4f5 · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.376354Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c3db9eee-199f-4cda-8508-3182bbe93860 · outbound

This paper cites Ex- tracting and composing robust features with denoising autoencoders.

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

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.381051Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 77197fc4-9df0-47df-94b6-9d405b5e984b · outbound

This paper cites What Regularized Auto-Encoders Learn from the Data Generating Distribution.

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

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.386113Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c648bd50-70d2-4639-9301-59b43eddc6ea · outbound

This paper cites Con- tracting auto-encoders: Explicit invariance during feature extraction.

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

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.390699Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b8f8fad1-9999-4315-9fa9-0d0d377e75d1 · outbound

This paper cites Kingma and Max Welling.

Deep Learning Models for Physical Layer Communications Kingma and Max Welling

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.395847Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 81789cdf-43a5-47c7-93a7-762e8aab131a · outbound

This paper cites Denoising diffusion probabilistic models.

Deep Learning Models for Physical Layer Communications Denoising diffusion probabilistic models

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.400626Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7b7a78df-10ee-49bc-941d-92b9b02ce400 · outbound

This paper cites Understanding diffusion models: A unified perspective, 2022.

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

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.405005Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 10e96339-6d5a-4fbd-9690-fe4c5462e0fd · outbound

This paper cites NICE: Non-linear Independent Components Estimation.

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

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.409114Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 658987f9-45b7-428d-b95f-eba322ce8e32 · outbound

This paper cites Kingma and Prafulla Dhariwal.

Deep Learning Models for Physical Layer Communications Kingma and Prafulla Dhariwal

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.413701Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 89550ec7-0bb3-4aa3-a010-e4bbc9ee1271 · outbound

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

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.419108Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 73ef4d0a-e5c7-4378-9497-f89b4695e77b · outbound

This paper cites Lawrence.

Deep Learning Models for Physical Layer Communications Lawrence

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.424141Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eeb2ebdd-ffe2-4525-b195-13430cda37df · outbound

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

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.428771Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1fcc873-099e-4350-813f-804da5907b33 · outbound

This paper cites Kschischang and Brendan J.

Deep Learning Models for Physical Layer Communications Kschischang and Brendan J

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.433082Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c018bd0a-37da-418f-95c9-67ed10a421d6 · outbound

This paper cites Deep autoregressive networks.

Deep Learning Models for Physical Layer Communications Deep autoregressive networks

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.438020Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c6ee07bb-24fc-42db-82c4-d67c2952ccab · outbound

This paper cites Conditional Image Generation with PixelCNN Decoders.

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

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.443301Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 254e6255-eb36-48cb-8dd3-f06c298a7db1 · outbound

This paper cites Attention is all you need.

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

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.448473Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 40355ca4-6354-4909-8a27-529713b1dce0 · outbound

This paper cites Hinton and Simon Osindero.

Deep Learning Models for Physical Layer Communications Hinton and Simon Osindero

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.452615Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ab59b23-e780-4650-8480-a4686fee4d24 · outbound

This paper cites Alain, Y.

Deep Learning Models for Physical Layer Communications Alain, Y

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.459330Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 67d0efd1-c401-4e0c-86da-833e162ae36d · outbound

This paper cites Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C.

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

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.463446Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ecd7fb36-03a8-48a6-b383-1975ecb29923 · outbound

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

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.467629Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5f658baf-64d4-42c9-a2a6-ab4d1e700df0 · outbound

This paper cites A style-based generator architecture for generative adversarial networks.

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

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.473439Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b67c2e05-11fd-4f02-86ae-a40d6f2f8933 · outbound

This paper cites f-gan: Training generative neural samplers using variational divergence minimization.

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

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.478288Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e5482db3-7c87-4925-a606-c5a852c47d88 · outbound

This paper cites Cario and Barry L.

Deep Learning Models for Physical Layer Communications Cario and Barry L

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.482641Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8349fde5-74ea-499e-b0a2-8b175e811ecb · outbound

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

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.488885Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 304e15bc-ec96-4abe-b33b-e9ce3e32c5ed · outbound

This paper cites an unresolved cited work.

Deep Learning Models for Physical Layer Communications Unresolved cited work

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.493271Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8993f587-c02f-42f4-9983-88e47b8f1272 · outbound

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

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.497945Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a141f12e-4f62-4d1e-bda0-35b27a7354ac · outbound

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

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.504314Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56bcee58-5bf2-4bf0-b429-30864df31cc8 · outbound

This paper cites an unresolved cited work.

Deep Learning Models for Physical Layer Communications Unresolved cited work

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.509865Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8040f852-3566-49e1-b960-dd734ed6be6f · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.514546Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a25179d-5f6e-4123-91f3-646d6604598e · outbound

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

Resolution: verified exact; local_arxiv, observed 2026-08-08T21:09:30.500956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.524334Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8edfce4a-4c83-4cf1-ae33-785cb253efc4 · outbound

This paper cites Borgwardt, Malte J.

Deep Learning Models for Physical Layer Communications Borgwardt, Malte J

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.528636Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 139f78ac-ab83-4747-8a2b-1ba83d402f64 · outbound

This paper cites Roy, and Zoubin Ghahramani.

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

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.533428Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e56b4fc-20bc-4285-9cda-e60f93419797 · outbound

This paper cites MNIST handwritten digit database.

Deep Learning Models for Physical Layer Communications MNIST handwritten digit database

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.538987Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9861dc06-c901-43d6-b34a-d5c27e48e305 · outbound

This paper cites Deeplearningfaceattributes in the wild.

Deep Learning Models for Physical Layer Communications Deeplearningfaceattributes in the wild

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.543553Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 79a610a9-e95f-4011-81bc-bbf7cea92a34 · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.547917Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 88912e89-4241-415f-b622-87fad9d05de8 · outbound

This paper cites an unresolved cited work.

Deep Learning Models for Physical Layer Communications Unresolved cited work

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.552472Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 98ac8a8b-481d-4541-ba68-6175751d1993 · outbound

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

Resolution: verified exact; local_arxiv, observed 2026-08-08T21:09:30.459274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation fc7b636d-54f0-4d24-ac87-346d039de241 · outbound

This paper cites Improved Techniques for Training GANs.

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

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.564193Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eba20998-5b72-4d28-b4ee-0b003fd5cbfb · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.568711Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 42e64341-c17a-4eb1-b0db-510148efeb68 · outbound

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

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

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.577733Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eaf7c144-024c-44a6-81c4-9b32f4c18a3e · outbound

This paper cites Rethinking the Inception Architecture for Computer Vision.

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

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.582615Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 91661bdb-506a-4728-b767-16b8f14075ab · outbound

This paper cites an unresolved cited work.

Deep Learning Models for Physical Layer Communications Unresolved cited work

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.587455Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 65f67e52-bb8b-45a4-bd61-e9825903fa9c · outbound

This paper cites Pixel recurrent neural networks.

Deep Learning Models for Physical Layer Communications Pixel recurrent neural networks

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.592177Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a8766bcd-cde1-4c25-a59d-6875c179eea5 · outbound

This paper cites Density estimation using real NVP.

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

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.597304Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 383aed95-4edd-4170-9044-7b60f0ea28e9 · outbound

This paper cites Wainwright, and Michael I.

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

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.602753Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

This paper cites an unresolved cited work.

Deep Learning Models for Physical Layer Communications Unresolved cited work

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.607169Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9ba034da-7289-4a46-a701-d0a5f3107e7d · outbound

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.616552Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 71a6cdb7-9402-4bde-aba3-2d5a49af636b · outbound

This paper cites Variational inference with normalizing flows.

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

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.620617Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e13152de-1499-49c3-b5c3-8922552c163a · outbound

This paper cites Wasserstein generative ad- versarial networks.

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

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.624961Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7d2571cc-b8e9-4df9-a049-2c1e5c6fbcc5 · outbound

This paper cites Generative moment matching networks.

Deep Learning Models for Physical Layer Communications Generative moment matching networks

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.629339Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 24fcf3a3-820b-499b-8cf4-f5ebf8405ef0 · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.633212Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 15ea3225-7a2d-4873-b664-7536abdab047 · outbound

This paper cites an unresolved cited work.

Deep Learning Models for Physical Layer Communications Unresolved cited work

Reference 94

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.637986Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c490db8b-b0cc-42f3-8952-61abafe51a3e · outbound

This paper cites an unresolved cited work.

Deep Learning Models for Physical Layer Communications Unresolved cited work

Reference 95

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.642676Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c1d2a2a-8eb9-4af8-98fe-28f4af1d2ff9 · outbound

This paper cites High-resolution image synthesis with latent diffusion models.

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

Reference 96

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.646814Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3358b858-43c0-45c2-babb-e505199572b0 · outbound

This paper cites Adversarial audio synthesis.

Deep Learning Models for Physical Layer Communications Adversarial audio synthesis

Reference 97

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.651710Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b395f41a-e3b5-4ee3-9aa2-3701987b48e0 · outbound

This paper cites Griffin and Jae Lim.

Deep Learning Models for Physical Layer Communications Griffin and Jae Lim

Reference 98

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.656445Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a04c1941-0675-40de-a456-f1f39ceaaa91 · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.660538Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d04f4b52-3d79-4b43-b0b8-bae7bd0d86af · outbound

This paper cites an unresolved cited work.

Deep Learning Models for Physical Layer Communications Unresolved cited work

Reference 100

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.665314Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 10097630-f0d0-4b1d-bbd1-0b3deb46b44a · outbound

This paper cites Mutual information neural estimation.

Deep Learning Models for Physical Layer Communications Mutual information neural estimation

Reference 101

Resolution: unresolved; no resolver link, observed 2026-08-08T21:09:29.670855Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers