Paper Citation Record · LEDGER

DenoMAE: A Multimodal Autoencoder for Denoising Modulation Signals

As of 18 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 1 inbound Pith citation observation for arXiv:2501.11538.

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

pith.paper-citation-record.v1
2501.11538 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T18:14:18.128087Z

measured 37 of 37 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 2026-08-16T10:58:55.345671Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-16T10:58:57.674327Z

Reference resolution

36 of 36 outbound references displayed

verified exact0
verified fuzzy27
unresolved9
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6d2c0014-606c-4143-a054-4a95f3e6c8c4 · outbound

This paper cites A cookbook of self-supervised learning,.

DenoMAE: A Multimodal Autoencoder for Denoising Modulation Signals A cookbook of self-supervised learning,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:14:18.646333Z

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 aec49aa5-32fb-418d-a299-a46475352c64 · outbound

This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer,.

DenoMAE: A Multimodal Autoencoder for Denoising Modulation Signals Exploring the limits of transfer learning with a unified text-to-text transformer,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:14:18.632533Z

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 2f5b704f-2076-4347-a605-1af440a44eb3 · outbound

This paper cites Emerging properties in self-supervised vision transformers,.

DenoMAE: A Multimodal Autoencoder for Denoising Modulation Signals Emerging properties in self-supervised vision transformers,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-10T18:14:17.870396Z

Source-reported events for the cited work

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Observation 0087f95a-2534-4a88-af39-343d2affd680 · outbound

This paper cites Learning transferable visual models from natural language supervision,.

DenoMAE: A Multimodal Autoencoder for Denoising Modulation Signals Learning transferable visual models from natural language supervision,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-10T18:14:17.875012Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5a617ed8-7cbf-4fad-a5ee-129d8e31d16b · outbound

This paper cites Deep learning-based snr estimation,.

DenoMAE: A Multimodal Autoencoder for Denoising Modulation Signals Deep learning-based snr estimation,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:14:18.601255Z

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 f77ff864-91c0-4d51-a34a-f8bc5173a420 · outbound

This paper cites Adaptive modulation with cazac preamble-based signal-to-noise-ratio estimator in ofdm cooperative com- munication system,.

DenoMAE: A Multimodal Autoencoder for Denoising Modulation Signals Adaptive modulation with cazac preamble-based signal-to-noise-ratio estimator in ofdm cooperative com- munication system,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:14:18.587669Z

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 1cc4c468-8a8c-4376-828d-8c57e16cc59f · outbound

This paper cites Learn- ing the unknown: Improving modulation classification performance in unseen scenarios,.

DenoMAE: A Multimodal Autoencoder for Denoising Modulation Signals Learn- ing the unknown: Improving modulation classification performance in unseen scenarios,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:14:18.574459Z

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 1ad1850c-cd29-444b-a67e-ab967332489c · outbound

This paper cites Adaptive spatial modulation mimo based on machine learning,.

DenoMAE: A Multimodal Autoencoder for Denoising Modulation Signals Adaptive spatial modulation mimo based on machine learning,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:14:18.561381Z

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 3a75aa1a-16c6-4c27-aa03-28debc813eca · outbound

This paper cites Gaf-mae: A self- supervised automatic modulation classification method based on gramian angular field and masked autoencoder,.

DenoMAE: A Multimodal Autoencoder for Denoising Modulation Signals Gaf-mae: A self- supervised automatic modulation classification method based on gramian angular field and masked autoencoder,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:14:18.548654Z

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 7350de48-b58d-4a03-afac-4390b8b1c3b3 · outbound

This paper cites Deep learning of radio frequency fingerprints from limited samples by masked autoencoding,.

DenoMAE: A Multimodal Autoencoder for Denoising Modulation Signals Deep learning of radio frequency fingerprints from limited samples by masked autoencoding,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:14:18.535462Z

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 f2b23d92-a23b-4827-8608-88876bf60c3e · outbound

This paper cites Multimae: Multi- modal multi-task masked autoencoders,.

DenoMAE: A Multimodal Autoencoder for Denoising Modulation Signals Multimae: Multi- modal multi-task masked autoencoders,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:14:18.521789Z

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 ff8ae4ea-832a-4896-b69e-bcf760a0c3a8 · outbound

This paper cites 4M-21: An Any-to-Any Vision Model for Tens of Tasks and Modalities.

DenoMAE: A Multimodal Autoencoder for Denoising Modulation Signals 4M-21: An Any-to-Any Vision Model for Tens of Tasks and Modalities

Reference 12

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Source-reported events for the cited work

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Observation b10429fb-ff35-4b75-a7cf-11572d26d791 · outbound

This paper cites Joint variational autoen- coders for multimodal imputation and embedding,.

DenoMAE: A Multimodal Autoencoder for Denoising Modulation Signals Joint variational autoen- coders for multimodal imputation and embedding,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:14:18.508610Z

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 ca969cbd-f03f-4ee9-9376-9a43217e4081 · outbound

This paper cites The dipencoder: Enforcing multimodality in autoencoders,.

DenoMAE: A Multimodal Autoencoder for Denoising Modulation Signals The dipencoder: Enforcing multimodality in autoencoders,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:14:18.494664Z

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 74ace1f2-8e2b-42b0-a796-2dc30c62f6a4 · outbound

This paper cites Fusion methods for cnn-based automatic modulation classification,.

DenoMAE: A Multimodal Autoencoder for Denoising Modulation Signals Fusion methods for cnn-based automatic modulation classification,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:14:18.480389Z

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 74d3ced2-2dab-4b3a-87fb-1016426ade91 · outbound

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

DenoMAE: A Multimodal Autoencoder for Denoising Modulation Signals Cnn-based automatic modulation classification for beyond 5g communications,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:14:18.466396Z

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 51f2c33d-698d-4a12-a6ae-1e9893e8eafb · outbound

This paper cites Modulation classification using convolutional neural network based deep learning model,.

DenoMAE: A Multimodal Autoencoder for Denoising Modulation Signals Modulation classification using convolutional neural network based deep learning model,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:14:18.452059Z

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 d2dc34c8-3815-459a-9300-a973e7bf5997 · outbound

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

DenoMAE: A Multimodal Autoencoder for Denoising Modulation Signals Mcnet: An efficient cnn architecture for robust automatic modulation classification,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:14:18.435422Z

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 621a3f84-83d9-4199-91be-6a1f9a31281c · outbound

This paper cites Automatic modulation recognition using deep learning architectures,.

DenoMAE: A Multimodal Autoencoder for Denoising Modulation Signals Automatic modulation recognition using deep learning architectures,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:14:18.421065Z

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 2f4992c8-3781-4f3b-aa68-453f3c4b8c3e · outbound

This paper cites Automatic modulation recognition using deep cvcnn-lstm architecture,.

DenoMAE: A Multimodal Autoencoder for Denoising Modulation Signals Automatic modulation recognition using deep cvcnn-lstm architecture,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:14:18.406802Z

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 1ca783fa-59cd-45ab-8b19-30b51b2e4018 · outbound

This paper cites Research on modulation recognition method in low snr based on lstm,.

DenoMAE: A Multimodal Autoencoder for Denoising Modulation Signals Research on modulation recognition method in low snr based on lstm,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:14:18.392499Z

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 7c8ea8b5-fd3d-42b3-809c-6eeff377036d · outbound

This paper cites Automatic modulation recognition based on cnn and gru,.

DenoMAE: A Multimodal Autoencoder for Denoising Modulation Signals Automatic modulation recognition based on cnn and gru,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:14:18.378613Z

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 5c8bbf97-a18b-4809-ac14-898e3e1f13fa · outbound

This paper cites A transformer-based contrastive semi-supervised learning framework for automatic modula- tion recognition,.

DenoMAE: A Multimodal Autoencoder for Denoising Modulation Signals A transformer-based contrastive semi-supervised learning framework for automatic modula- tion recognition,

Reference 23

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Source-reported events for the cited work

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Observation 1a5c3548-ee7a-4b17-ade0-e003e22eabb9 · outbound

This paper cites A transformer-based ctdnn structure for automatic modulation recognition,.

DenoMAE: A Multimodal Autoencoder for Denoising Modulation Signals A transformer-based ctdnn structure for automatic modulation recognition,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:14:18.353785Z

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 00961abd-f25a-4e4f-867e-4db72974cb12 · outbound

This paper cites Signal modulation classification based on the transformer network,.

DenoMAE: A Multimodal Autoencoder for Denoising Modulation Signals Signal modulation classification based on the transformer network,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-10T18:14:18.084525Z

Source-reported events for the cited work

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Observation 61433ea0-f0b5-4c6b-8ef9-e57040f74c33 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

DenoMAE: A Multimodal Autoencoder for Denoising Modulation Signals BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-10T18:14:18.088404Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a627226-7414-4d20-8f95-6820bc985c6c · outbound

This paper cites Masked Autoencoders Are Scalable Vision Learners.

DenoMAE: A Multimodal Autoencoder for Denoising Modulation Signals Masked Autoencoders Are Scalable Vision Learners

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-10T18:14:18.092619Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 81fbe952-2d68-4d75-af41-373601d3ad77 · outbound

This paper cites Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks,.

DenoMAE: A Multimodal Autoencoder for Denoising Modulation Signals Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-10T18:14:18.096630Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6f9a7629-c319-4c17-8068-4a77f3b7bc3f · outbound

This paper cites Uniter: Universal image-text representation learning,.

DenoMAE: A Multimodal Autoencoder for Denoising Modulation Signals Uniter: Universal image-text representation learning,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:14:18.319359Z

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 de348411-20ef-4635-9dde-c6dd556c6412 · outbound

This paper cites Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks.

DenoMAE: A Multimodal Autoencoder for Denoising Modulation Signals Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-10T18:14:18.104519Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 08c58389-d804-4fdc-8bc0-fa98fb57d06c · outbound

This paper cites 4m: Massively multimodal masked modeling,.

DenoMAE: A Multimodal Autoencoder for Denoising Modulation Signals 4m: Massively multimodal masked modeling,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:14:18.304019Z

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 6d2b730b-8f24-4066-8147-0b2f0aaa1232 · outbound

This paper cites Learning constellation map with deep cnn for accurate modulation recognition,.

DenoMAE: A Multimodal Autoencoder for Denoising Modulation Signals Learning constellation map with deep cnn for accurate modulation recognition,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:14:18.287071Z

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 6991d458-e49f-4e7c-8328-0ebdebb0634d · outbound

This paper cites Deep learning for constellation-based modulation classifica- tion under multipath fading channels,.

DenoMAE: A Multimodal Autoencoder for Denoising Modulation Signals Deep learning for constellation-based modulation classifica- tion under multipath fading channels,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:14:18.271282Z

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 5822849e-4ea8-42c3-a0e9-1652d05871dc · outbound

This paper cites Modulation classification based on signal constellation diagrams and deep learning,.

DenoMAE: A Multimodal Autoencoder for Denoising Modulation Signals Modulation classification based on signal constellation diagrams and deep learning,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:14:18.256596Z

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 1606cd68-0bb7-48d4-8a66-90885f81482d · outbound

This paper cites Automatic modulation classification: A deep learning enabled approach,.

DenoMAE: A Multimodal Autoencoder for Denoising Modulation Signals Automatic modulation classification: A deep learning enabled approach,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:14:18.241406Z

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 e66a2fcc-6c6f-4971-ad9a-0b6e8f90f092 · outbound

This paper cites Automatic modulation classification using techniques from image classification,.

DenoMAE: A Multimodal Autoencoder for Denoising Modulation Signals Automatic modulation classification using techniques from image classification,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:14:18.226712Z

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 d595c64a-329c-4d76-93d7-5ef9055b210d · inbound

Unsupervised Time-Series Signal Analysis with Autoencoders and Vision Transformers: A Review of Architectures and Applications cites this paper.

Unsupervised Time-Series Signal Analysis with Autoencoders and Vision Transformers: A Review of Architectures and Applications DenoMAE: A Multimodal Autoencoder for Denoising Modulation Signals

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-08-16T10:58:57.678052Z

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.