Paper Citation Record · LEDGER

AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities

As of 18 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 1 inbound Pith citation observation for arXiv:2502.05315.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2502.05315 v2

Coverage vector

measured 53 of 53 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T19:51:25.910597Z

measured 54 of 54 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T00:15:37.783925Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-05T00:15:38.365040Z

Reference resolution

53 of 53 outbound references displayed

verified exact0
verified fuzzy50
unresolved3
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e46c999d-3ca5-49de-bd36-f6c11911ba9c · outbound

This paper cites A lightweight automatic modulation recognition algorithm based on deep learning.

AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities A lightweight automatic modulation recognition algorithm based on deep learning

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:26.493051Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6119494b-668e-4c32-8d6d-1ec1df116bdb · outbound

This paper cites Deep learning based automatic modulation recognition: Models, datasets, and challenges.

AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities Deep learning based automatic modulation recognition: Models, datasets, and challenges

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:26.484891Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a89355ac-9f7e-4876-9ab2-73f5f30e6bc3 · outbound

This paper cites Automatic modulation recognition of radar signals based on histogram of oriented gradient via improved principal component analysis.

AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities Automatic modulation recognition of radar signals based on histogram of oriented gradient via improved principal component analysis

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:26.476780Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b7adaf80-7ecd-41f4-8920-4aae6dbc7ef0 · outbound

This paper cites Smart sensing and context -cognitive networking in and beyond mmwave band: Efficiency, reliability, and security.

AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities Smart sensing and context -cognitive networking in and beyond mmwave band: Efficiency, reliability, and security

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:26.468225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 184a60e2-e74f-4ae9-b07c-57a30b7c0b3f · outbound

This paper cites Lightweight deep learning model for automatic modulation classification in cognitive radio networks.

AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities Lightweight deep learning model for automatic modulation classification in cognitive radio networks

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:26.459785Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2bf0fe68-7101-4dc5-9962-058c37b8b506 · outbound

This paper cites Evelyn Ezhilarasi, J.

AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities Evelyn Ezhilarasi, J

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:26.451137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2eb7a089-1284-480f-a082-c00fa10c453f · outbound

This paper cites Learning the speech front -end with raw waveform cldnns.

AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities Learning the speech front -end with raw waveform cldnns

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:26.442828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2de0401b-fa47-4680-9338-90c7807655e2 · outbound

This paper cites Enhancing Automatic Modulation Recognition through Robust Global Feature Extraction.

AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities Enhancing Automatic Modulation Recognition through Robust Global Feature Extraction

Reference 8

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Source-reported events for the cited work

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Observation 283b6dce-9e38-4bf3-b7c2-dcf69db276e6 · outbound

This paper cites A deep learning based algorithm with multi- level feature extraction for automatic modulation recognition.

AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities A deep learning based algorithm with multi- level feature extraction for automatic modulation recognition

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:26.434722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4a0b32fc-864d-4570-b723-f221529a179e · outbound

This paper cites Deep learning radio frequency signal classification with hybrid images.

AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities Deep learning radio frequency signal classification with hybrid images

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:26.426010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 74efc88d-bcde-4473-8d8b-7921747e79f6 · outbound

This paper cites Amc -net: An effective network for automatic modulation classification.

AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities Amc -net: An effective network for automatic modulation classification

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:26.417820Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation aceddb01-7aa5-4548-b6cb-e49920806ccb · outbound

This paper cites Deep learning based automatic modulation recognition: Models, datasets, and challenges.

AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities Deep learning based automatic modulation recognition: Models, datasets, and challenges

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:26.409842Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5fb1dcec-d763-4f19-9a63-848830f4f475 · outbound

This paper cites Gravelle.

AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities Gravelle

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:26.401609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1da9f875-2082-4751-a662-c35f064688cc · outbound

This paper cites Over-the-air deep learning based radio signal classification.

AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities Over-the-air deep learning based radio signal classification

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:26.393694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 635c1cef-40f6-453d-9bbf-1867ede67442 · outbound

This paper cites Introduction to algogens, 2024.

AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities Introduction to algogens, 2024

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:26.385575Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 415f863e-a815-43e5-ad1b-80ba5f03e2ff · outbound

This paper cites Convolutional radio modulation recognition networks.

AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities Convolutional radio modulation recognition networks

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:26.377210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 80d465a4-2ca3-458c-b6a8-8715c7ede0b4 · outbound

This paper cites Robust and fast automatic modulation classification with cnn under multipath fading channels.

AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities Robust and fast automatic modulation classification with cnn under multipath fading channels

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:26.368925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 56711cc1-58e6-4275-b45d-d5f53c7d847c · outbound

This paper cites Deep architectures for modulation recognition.

AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities Deep architectures for modulation recognition

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:26.360277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation aea458a4-dcbd-45d5-b142-89918f0e64e8 · outbound

This paper cites Cnn -based automatic modulation classification for beyond 5g communications.

AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities Cnn -based automatic modulation classification for beyond 5g communications

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:26.352066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 26474c76-ea5a-4978-9190-95377fd619f4 · outbound

This paper cites Mcnet: An efficient cnn architecture for robust automatic modulation classification.

AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities Mcnet: An efficient cnn architecture for robust automatic modulation classification

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:26.343827Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 67305618-74d9-4503-8e68-419870a65079 · outbound

This paper cites Deep learning models for wireless signal classification with distributed low -cost spectrum sensors.

AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities Deep learning models for wireless signal classification with distributed low -cost spectrum sensors

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:26.335692Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 71e58bfd-e01a-49e8-bf1b-6e7a94d7ef20 · outbound

This paper cites Automatic modulation classification using recurrent neural networks.

AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities Automatic modulation classification using recurrent neural networks

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:26.327702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 06c73c2a-e1f3-4619-b28b-5607c99d355e · outbound

This paper cites A spatiotemporal multi- channel learning framework for automatic modulation recognition.

AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities A spatiotemporal multi- channel learning framework for automatic modulation recognition

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:26.319825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 62f956c3-a0ff-4838-a27a-b18b163ce29e · outbound

This paper cites Cgdnet: Efficient hybrid deep learning model for robust automatic modulation recognition.

AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities Cgdnet: Efficient hybrid deep learning model for robust automatic modulation recognition

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:26.311530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0a9f0573-c6ba-4173-8311-35454dacc44e · outbound

This paper cites Modulation recognition of underwater acoustic communication signals based on neural architecture search.

AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities Modulation recognition of underwater acoustic communication signals based on neural architecture search

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:26.303132Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8482ff59-0e67-48e5-8ded-9a5498676775 · outbound

This paper cites An efficient deep learning model for automatic modulation recognition based on parameter estimation and transformation.

AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities An efficient deep learning model for automatic modulation recognition based on parameter estimation and transformation

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:26.294651Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6d984786-1bde-497c-9416-1a22e6706bb1 · outbound

This paper cites Data - transform multi-channel hybrid deep learning for automatic modulation recognition.

AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities Data - transform multi-channel hybrid deep learning for automatic modulation recognition

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:26.286341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 706ce57b-ae01-4b5b-ab3b-b8cc95f9686a · outbound

This paper cites Rfnet: Fast and efficient neural network for modulation classification of radio frequency signals.

AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities Rfnet: Fast and efficient neural network for modulation classification of radio frequency signals

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:26.277991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e111a915-b7ee-4f4e-b6e1-e54c73fcbbb7 · outbound

This paper cites Lightweight deep learning model for automatic modulation classification in cognitive radio networks.

AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities Lightweight deep learning model for automatic modulation classification in cognitive radio networks

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:26.269491Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 57306744-31d6-4a3f-a512-6ee125b027d4 · outbound

This paper cites A comparative study between cnn, lstm, and cldnn models in the context of radio modulation classification.

AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities A comparative study between cnn, lstm, and cldnn models in the context of radio modulation classification

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:26.260993Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 29cadb32-d82d-4804-9d90-43dbcbd33d90 · outbound

This paper cites Radio modulation classification using deep residual neural networks.

AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities Radio modulation classification using deep residual neural networks

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:26.252559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 251b4115-4f81-45a0-beb8-879739ac6b64 · outbound

This paper cites Deep convolutional neural networks for double compressed amr audio detection.

AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities Deep convolutional neural networks for double compressed amr audio detection

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:26.243972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 017d273b-dad4-4c11-84f6-9410e143d7a2 · outbound

This paper cites A novel bigrubilstm model for multilevel sentiment analysis using deep neural network with bigru-bilstm.

AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities A novel bigrubilstm model for multilevel sentiment analysis using deep neural network with bigru-bilstm

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:26.235709Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d6aa1c3e-0b71-4d51-9d62-5969aeffaa1c · outbound

This paper cites Mec: Memory-efficient convolution for deep neural network.

AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities Mec: Memory-efficient convolution for deep neural network

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:26.226928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 466bdc86-b50a-44b9-9aef-189d616b9711 · outbound

This paper cites Learning phrase representations using rnn encoder -decoder for statistical machine translation, 2014.

AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities Learning phrase representations using rnn encoder -decoder for statistical machine translation, 2014

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:26.218781Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 537420d3-c023-48cc-8954-11c7cd7d5b49 · outbound

This paper cites Network intrusion detection model based on cnn and gru.

AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities Network intrusion detection model based on cnn and gru

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:26.210441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c21f1488-73a8-4fc8-888d-9beb233f2907 · outbound

This paper cites Cnn -bilstm-dnn-based modulation recognition algorithm at low snr.

AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities Cnn -bilstm-dnn-based modulation recognition algorithm at low snr

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:26.201354Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4ab2c772-9072-4f6f-aac4-8d3831dbf565 · outbound

This paper cites Github - thienhuynhthe/mcnet: Mcnet: An efficient cnn architecture for robust automatic modulation classification.

AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities Github - thienhuynhthe/mcnet: Mcnet: An efficient cnn architecture for robust automatic modulation classification

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:26.193392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a2d9da2b-8e2e-4ad1-b2a4-86a872011895 · outbound

This paper cites Intrusion detection systems using long short-term memory (lstm).

AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities Intrusion detection systems using long short-term memory (lstm)

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:26.185126Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 83c12a07-0ee4-4223-9625-fdac10e58f28 · outbound

This paper cites Getting the most out of amr parsing.

AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities Getting the most out of amr parsing

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:26.176746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 72ff3dfc-a879-4b4b-a61a-d6b7f1b880a0 · outbound

This paper cites Artificial neural network based epileptic detection using time -domain and frequency - domain features.

AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities Artificial neural network based epileptic detection using time -domain and frequency - domain features

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:26.168523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1753f8ab-84fb-483e-8893-1b738a9b9482 · outbound

This paper cites On optimal frequency - domain multichannel linear filtering for noise reduction.

AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities On optimal frequency - domain multichannel linear filtering for noise reduction

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:26.159178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ba2d7b41-9434-42dc-84e0-36f9226ca63c · outbound

This paper cites Synthetic data generation: A comparative study.

AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities Synthetic data generation: A comparative study

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:26.150570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7b60c301-f314-49cb-a7cb-53382ee8d74c · outbound

This paper cites Survey on synthetic data generation, evaluation methods and gans.

AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities Survey on synthetic data generation, evaluation methods and gans

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:26.141942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ddd578fb-cd2f-4644-9488-db482af0f944 · outbound

This paper cites Recent progress on generative adversarial networks (gans): A survey.

AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities Recent progress on generative adversarial networks (gans): A survey

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:26.133172Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 810122b2-e3ae-403c-a109-59d0a07b2d3f · outbound

This paper cites Tutorial on Variational Autoencoders.

AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities Tutorial on Variational Autoencoders

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-08T19:51:25.887647Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5cd1bd43-006c-45b1-84e9-3881852d3ad7 · outbound

This paper cites Analysis of classifier training on synthetic data for cross - domain datasets.

AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities Analysis of classifier training on synthetic data for cross - domain datasets

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:26.123697Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 10c901a6-ce2f-43bf-907c-1437e2c691da · outbound

This paper cites IEEE Access, 8:196197–196211, 2020.

AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities IEEE Access, 8:196197–196211, 2020

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:26.114012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 672353ce-1bcb-4c7f-99f3-522aa75c0b06 · outbound

This paper cites Codetf: One -stop transformer library for state -of-the-art code llm.

AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities Codetf: One -stop transformer library for state -of-the-art code llm

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-08T19:51:25.897232Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 63a12060-eee4-4cb7-896e-18c2d5a813de · outbound

This paper cites Deep hybrid transformer network for robust modulation classification in wireless communications.

AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities Deep hybrid transformer network for robust modulation classification in wireless communications

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:26.104494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7f0f8d00-31c7-41e8-8113-15deaa9349e3 · outbound

This paper cites Long short -term memory.

AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities Long short -term memory

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:26.095566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a81a0242-8a59-4893-9641-e65f279c4daa · outbound

This paper cites Fully decentralized federated learning.

AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities Fully decentralized federated learning

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:26.086559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 56cd3dc2-c474-4293-870d-1ef109224356 · outbound

This paper cites Federated learning for automatic modulation classification under class imbalance and varying noise condition.

AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities Federated learning for automatic modulation classification under class imbalance and varying noise condition

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:26.077164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

Observation b751c986-e1f5-4d9b-964c-6c8aa1385fa9 · inbound

An Uncertainty-Driven Hybrid Deep Learning Approach for Broad-Coverage RF Modulation Recognition cites this paper.

An Uncertainty-Driven Hybrid Deep Learning Approach for Broad-Coverage RF Modulation Recognition AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-08-05T00:15:38.427070Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.