Paper Citation Record · LEDGER

An Uncertainty-Driven Hybrid Deep Learning Approach for Broad-Coverage RF Modulation Recognition

As of 18 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2608.00796.

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

pith.paper-citation-record.v1
2608.00796 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T00:15:37.846689Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

25 of 25 outbound references displayed

verified exact6
verified fuzzy1
unresolved6
parse uncertain0
malformed identifier3
metadata mismatch9

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6da90b3f-cf72-4bad-9084-4cb3b36b3f03 · outbound

This paper cites Survey of automatic modulation classification techniques: Classical approaches and new trends,.

An Uncertainty-Driven Hybrid Deep Learning Approach for Broad-Coverage RF Modulation Recognition Survey of automatic modulation classification techniques: Classical approaches and new trends,

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5b2c2705-1bd9-4d14-a1c8-fd12372707a6 · outbound

This paper cites An introduction to deep learning for the physical layer,.

An Uncertainty-Driven Hybrid Deep Learning Approach for Broad-Coverage RF Modulation Recognition An introduction to deep learning for the physical layer,

Reference 2

Resolution: metadata mismatch; raw_fallback, observed 2026-08-05T00:15:41.758093Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 191804d2-edb9-40f0-a5a5-a21f645190d1 · outbound

This paper cites Deep architectures for modulation recog- nition,.

An Uncertainty-Driven Hybrid Deep Learning Approach for Broad-Coverage RF Modulation Recognition Deep architectures for modulation recog- nition,

Reference 3

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7ce97327-f8eb-46d0-bd0b-b1a7438e50bb · outbound

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

An Uncertainty-Driven Hybrid Deep Learning Approach for Broad-Coverage RF Modulation Recognition Over-the-air deep learning based radio signal classification,

Reference 4

Resolution: metadata mismatch; raw_fallback, observed 2026-08-05T00:15:41.150119Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 976f3d70-d3f3-44a5-8e1e-84ff1aa447a4 · outbound

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

An Uncertainty-Driven Hybrid Deep Learning Approach for Broad-Coverage RF Modulation Recognition Deep learning models for wireless signal classification with distributed low- cost spectrum sensors,

Reference 5

Resolution: metadata mismatch; raw_fallback, observed 2026-08-05T00:15:40.911005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation cd6845f5-5dbf-4b32-8b45-0334a2b548c6 · outbound

This paper cites Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning.

An Uncertainty-Driven Hybrid Deep Learning Approach for Broad-Coverage RF Modulation Recognition Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-05T00:15:36.516704Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d2fb27ff-e0df-4cf1-b92c-43e9f9c432ce · outbound

This paper cites Deep neural network architectures for modulation classification,.

An Uncertainty-Driven Hybrid Deep Learning Approach for Broad-Coverage RF Modulation Recognition Deep neural network architectures for modulation classification,

Reference 7

Resolution: metadata mismatch; raw_fallback, observed 2026-08-05T00:15:40.551199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8ef72f96-f163-427b-8adf-ebbbe55b5a63 · outbound

This paper cites Spectrum analysis and convolutional neural network for automatic modulation recognition,.

An Uncertainty-Driven Hybrid Deep Learning Approach for Broad-Coverage RF Modulation Recognition Spectrum analysis and convolutional neural network for automatic modulation recognition,

Reference 8

Resolution: metadata mismatch; raw_fallback, observed 2026-08-05T00:15:40.296944Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7bf1ee37-b750-4cac-b5fb-80df1068332b · outbound

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

An Uncertainty-Driven Hybrid Deep Learning Approach for Broad-Coverage RF Modulation Recognition A spatiotemporal multi- channel learning framework for automatic modulation recognition,

Reference 9

Resolution: metadata mismatch; raw_fallback, observed 2026-08-05T00:15:40.076847Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5dd85378-5218-4243-ba4d-0ae1a88d7b74 · outbound

This paper cites A survey of modulation classification using deep learning: Signal representation and data preprocessing,.

An Uncertainty-Driven Hybrid Deep Learning Approach for Broad-Coverage RF Modulation Recognition A survey of modulation classification using deep learning: Signal representation and data preprocessing,

Reference 10

Resolution: metadata mismatch; raw_fallback, observed 2026-08-05T00:15:39.891763Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e9430a1e-f86a-4b1e-a231-6c2ff033d73d · outbound

This paper cites Sequential convolu- tional recurrent neural networks for fast automatic modulation classifi- cation,.

An Uncertainty-Driven Hybrid Deep Learning Approach for Broad-Coverage RF Modulation Recognition Sequential convolu- tional recurrent neural networks for fast automatic modulation classifi- cation,

Reference 11

Resolution: malformed identifier; no resolver link, observed 2026-08-05T00:15:36.893215Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 99dfde42-1b77-4995-848b-69fe4cad4c7f · outbound

This paper cites Deep learn- ing based automatic modulation recognition: Models, datasets, and challenges,.

An Uncertainty-Driven Hybrid Deep Learning Approach for Broad-Coverage RF Modulation Recognition Deep learn- ing based automatic modulation recognition: Models, datasets, and challenges,

Reference 12

Resolution: metadata mismatch; raw_fallback, observed 2026-08-05T00:15:39.534322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 38a3243a-396f-43d2-85eb-c081e4656031 · outbound

This paper cites Automatic modu- lation classification based on CNN-Transformer graph neural network,.

An Uncertainty-Driven Hybrid Deep Learning Approach for Broad-Coverage RF Modulation Recognition Automatic modu- lation classification based on CNN-Transformer graph neural network,

Reference 13

Resolution: verified exact; doi, observed 2026-08-05T00:15:38.017100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c3b4202a-b48b-4690-9645-11078f913812 · outbound

This paper cites LightAMC: Lightweight automatic modulation classification via deep learning and compressive sensing,.

An Uncertainty-Driven Hybrid Deep Learning Approach for Broad-Coverage RF Modulation Recognition LightAMC: Lightweight automatic modulation classification via deep learning and compressive sensing,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:15:41.950176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4dd16e1a-1b52-474d-bbc9-83c07c3aaa64 · outbound

This paper cites What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?.

An Uncertainty-Driven Hybrid Deep Learning Approach for Broad-Coverage RF Modulation Recognition What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-05T00:15:37.257267Z

Source-reported events for the cited work

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Observation 28d49667-e38b-4542-b7f5-2ffccd5747e5 · outbound

This paper cites Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles.

An Uncertainty-Driven Hybrid Deep Learning Approach for Broad-Coverage RF Modulation Recognition Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-05T00:15:37.345502Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cbfae79f-e130-42dd-a63f-cfca8ebd26df · outbound

This paper cites An uncertainty quantification frame- work for deep learning-based automatic modulation classification,.

An Uncertainty-Driven Hybrid Deep Learning Approach for Broad-Coverage RF Modulation Recognition An uncertainty quantification frame- work for deep learning-based automatic modulation classification,

Reference 17

Resolution: verified exact; raw_fallback, observed 2026-08-05T00:15:39.040541Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 729f43ad-2694-4ea8-9d21-278e9b7287f5 · outbound

This paper cites Open Set Wireless Signal Classification: Augmenting Deep Learning with Expert Feature Classifiers.

An Uncertainty-Driven Hybrid Deep Learning Approach for Broad-Coverage RF Modulation Recognition Open Set Wireless Signal Classification: Augmenting Deep Learning with Expert Feature Classifiers

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-08-05T00:15:38.791625Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c4dcf12e-4fd5-472c-be86-517cb6c64acc · outbound

This paper cites Class Information Guided Reconstruction for Automatic Modulation Open-Set Recognition.

An Uncertainty-Driven Hybrid Deep Learning Approach for Broad-Coverage RF Modulation Recognition Class Information Guided Reconstruction for Automatic Modulation Open-Set Recognition

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-05T00:15:38.652239Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation dc7fd40f-2260-416a-8f2e-69ecb28e099a · outbound

This paper cites A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks.

An Uncertainty-Driven Hybrid Deep Learning Approach for Broad-Coverage RF Modulation Recognition A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-05T00:15:37.626436Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4fa19d4e-9af3-4d1b-93fc-cc0bd8e185c2 · outbound

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

An Uncertainty-Driven Hybrid Deep Learning Approach for Broad-Coverage RF Modulation Recognition Enhancing Automatic Modulation Recognition through Robust Global Feature Extraction

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-05T00:15:37.665891Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cb5accf6-a489-4984-aaf2-b2f6a859d195 · outbound

This paper cites MAMCA -- Optimal on Accuracy and Efficiency for Automatic Modulation Classification with Extended Signal Length.

An Uncertainty-Driven Hybrid Deep Learning Approach for Broad-Coverage RF Modulation Recognition MAMCA -- Optimal on Accuracy and Efficiency for Automatic Modulation Classification with Extended Signal Length

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-05T00:15:37.718131Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

This paper cites AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities.

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 2026-08-17T06:30:58.91139+00:00.

Observation bc4a3de4-567c-467f-b584-b3fba4a621b3 · outbound

This paper cites CNN-LSTM hybrid architecture for over-the-air automatic modulation classification using SDR,.

An Uncertainty-Driven Hybrid Deep Learning Approach for Broad-Coverage RF Modulation Recognition CNN-LSTM hybrid architecture for over-the-air automatic modulation classification using SDR,

Reference 24

Resolution: verified exact; raw_fallback, observed 2026-08-05T00:15:38.274252Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0588b455-042d-411d-8425-b28b9872fb5a · outbound

This paper cites an unresolved cited work.

An Uncertainty-Driven Hybrid Deep Learning Approach for Broad-Coverage RF Modulation Recognition Unresolved cited work

Reference 2020

Resolution: metadata mismatch; raw_fallback, observed 2026-08-05T00:15:39.302974Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Pith citing papers

No inbound Pith citation observations are available.