Paper Citation Record · LEDGER

Erasing Noise in Signal Detection with Diffusion Model: From Theory to Application

As of 15 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 2 inbound Pith citation observations for arXiv:2501.07030.

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

pith.paper-citation-record.v1
2501.07030 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T20:57:04.759641Z

measured 24 of 24 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T05:35:39.423834Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-25T03:06:34.011465Z

Reference resolution

22 of 22 outbound references displayed

verified exact0
verified fuzzy14
unresolved8
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b4f3e8f0-2a34-4e87-b069-083c5743de1f · outbound

This paper cites A mathematical theory of communication,.

Erasing Noise in Signal Detection with Diffusion Model: From Theory to Application A mathematical theory of communication,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-10T20:57:04.661155Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 37cee238-3c43-442b-b05d-01e6596cc13c · outbound

This paper cites Efficient qam signal detector for massive mimo systems via ps/dps-admm approaches,.

Erasing Noise in Signal Detection with Diffusion Model: From Theory to Application Efficient qam signal detector for massive mimo systems via ps/dps-admm approaches,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:57:05.066796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9d1a9ca7-aad6-4b63-a09b-a097e5bb73c4 · outbound

This paper cites Optimal noise benefits in neyman–pearson and inequality-constrained statistical signal detection,.

Erasing Noise in Signal Detection with Diffusion Model: From Theory to Application Optimal noise benefits in neyman–pearson and inequality-constrained statistical signal detection,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:57:05.051049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 057193e2-055e-480a-9d22-ae269d5ec9da · outbound

This paper cites A new deep learning framework for hf signal detection in wideband spectrogram,.

Erasing Noise in Signal Detection with Diffusion Model: From Theory to Application A new deep learning framework for hf signal detection in wideband spectrogram,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:57:05.036540Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1beeb8b9-8f84-4514-8c63-53349d8401dd · outbound

This paper cites Maximum-likelihood estimation of parameters of signal-detection theory and determination of confidence intervals—rating-method data,.

Erasing Noise in Signal Detection with Diffusion Model: From Theory to Application Maximum-likelihood estimation of parameters of signal-detection theory and determination of confidence intervals—rating-method data,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:57:05.022122Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 917d8489-84be-42c1-807d-dd2e9ee7dc7c · outbound

This paper cites Symbol denoising in high order m-qam using residual learning of deep cnn,.

Erasing Noise in Signal Detection with Diffusion Model: From Theory to Application Symbol denoising in high order m-qam using residual learning of deep cnn,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:57:05.007970Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 525b7fa1-ddd1-4803-aa23-565d948d586c · outbound

This paper cites Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising,.

Erasing Noise in Signal Detection with Diffusion Model: From Theory to Application Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T20:57:04.691985Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f56500c2-0fc5-4640-a969-1cfea16c91f0 · outbound

This paper cites Comnet: Combination of deep learning and expert knowledge in ofdm receivers,.

Erasing Noise in Signal Detection with Diffusion Model: From Theory to Application Comnet: Combination of deep learning and expert knowledge in ofdm receivers,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:57:04.984363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation cbbfcbb5-178a-4e0f-8300-3acd54ce4cb0 · outbound

This paper cites Sigt: An efficient end-to-end mimo-ofdm receiver framework based on transformer,.

Erasing Noise in Signal Detection with Diffusion Model: From Theory to Application Sigt: An efficient end-to-end mimo-ofdm receiver framework based on transformer,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:57:04.970070Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7c1f7a94-00be-41f6-93d8-914b528aabf2 · outbound

This paper cites Message passing meets graph neural networks: A new paradigm for massive mimo systems,.

Erasing Noise in Signal Detection with Diffusion Model: From Theory to Application Message passing meets graph neural networks: A new paradigm for massive mimo systems,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:57:04.955156Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 84ccfc40-92b4-4a57-ac09-702cc913c802 · outbound

This paper cites Goodfellow, Y.

Erasing Noise in Signal Detection with Diffusion Model: From Theory to Application Goodfellow, Y

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-10T20:57:04.710761Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c795370c-cc5c-4dff-a907-3f5c1f4e40f4 · outbound

This paper cites Generalized odin: Detecting out-of-distribution image without learning from out-of-distribution data,.

Erasing Noise in Signal Detection with Diffusion Model: From Theory to Application Generalized odin: Detecting out-of-distribution image without learning from out-of-distribution data,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:57:04.930865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation fc9d13ae-bdec-4918-aa3b-d48d7ab66ac6 · outbound

This paper cites Denoising diffusion probabilistic models,.

Erasing Noise in Signal Detection with Diffusion Model: From Theory to Application Denoising diffusion probabilistic models,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-10T20:57:04.719920Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9b0b4aba-22c2-4291-bc1f-770775048c42 · outbound

This paper cites DiffBIR: Towards Blind Image Restoration with Generative Diffusion Prior.

Erasing Noise in Signal Detection with Diffusion Model: From Theory to Application DiffBIR: Towards Blind Image Restoration with Generative Diffusion Prior

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T20:57:04.723854Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 897c7c62-dd37-4c5c-a305-79c44fae3f34 · outbound

This paper cites Structured denoising diffusion models in discrete state-spaces,.

Erasing Noise in Signal Detection with Diffusion Model: From Theory to Application Structured denoising diffusion models in discrete state-spaces,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:57:04.905986Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 71c70552-5381-4054-89c8-791f1f0db8a4 · outbound

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

Erasing Noise in Signal Detection with Diffusion Model: From Theory to Application High-resolution image synthesis with latent diffusion models,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-10T20:57:04.732955Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e16602f-e0ba-40dd-844f-8a062a24f57c · outbound

This paper cites Score-Based Generative Modeling through Stochastic Differential Equations.

Erasing Noise in Signal Detection with Diffusion Model: From Theory to Application Score-Based Generative Modeling through Stochastic Differential Equations

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-10T20:57:04.737116Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b6c7a744-6071-4edb-a7a6-951ebc843f53 · outbound

This paper cites Decoupled diffusion models: Simultaneous image to zero and zero to noise,.

Erasing Noise in Signal Detection with Diffusion Model: From Theory to Application Decoupled diffusion models: Simultaneous image to zero and zero to noise,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:57:04.882695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 721dcf2b-1b14-44a5-bcfb-848273407d3c · outbound

This paper cites Blended diffusion for text-driven editing of natural images,.

Erasing Noise in Signal Detection with Diffusion Model: From Theory to Application Blended diffusion for text-driven editing of natural images,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:57:04.867996Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ebbd0e66-fbd7-46fa-8ffc-e48283d846c6 · outbound

This paper cites Attention is all you need,.

Erasing Noise in Signal Detection with Diffusion Model: From Theory to Application Attention is all you need,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-10T20:57:04.750737Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6ec1241c-bfc0-4550-a966-1e4f6b85c765 · outbound

This paper cites Exploiting radio fingerprints for simultaneous localization and mapping,.

Erasing Noise in Signal Detection with Diffusion Model: From Theory to Application Exploiting radio fingerprints for simultaneous localization and mapping,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:57:04.843131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a82a56b5-9b0d-43cb-bb55-ea51db007b0f · outbound

This paper cites Adversarial variational bayes: Unifying variational autoencoders and generative adversarial networks,.

Erasing Noise in Signal Detection with Diffusion Model: From Theory to Application Adversarial variational bayes: Unifying variational autoencoders and generative adversarial networks,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:57:04.827146Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Pith citing papers

Observation 255edbdf-08a7-4a0a-b74c-569706d07607 · inbound

Censored Sampling for Topology Design: Guiding Diffusion with Human Preferences cites this paper.

Censored Sampling for Topology Design: Guiding Diffusion with Human Preferences Erasing Noise in Signal Detection with Diffusion Model: From Theory to Application

Reference 115

Resolution: unresolved; no resolver link, observed 2026-08-06T05:35:39.423834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 84323a8f-713f-444d-a0bd-b142dc19dc4a · inbound

Diffusion Fluid Antenna Systems for Resilient ISAC cites this paper.

Diffusion Fluid Antenna Systems for Resilient ISAC Erasing Noise in Signal Detection with Diffusion Model: From Theory to Application

Reference 77

Resolution: verified exact; arxiv_id, observed 2026-05-25T03:06:34.015185Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.