Paper Citation Record · LEDGER

Unsupervised Online Channel Estimation for High-Mobility OFDM via Implicit Neural Representation

As of 4 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2605.10213.

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

pith.paper-citation-record.v1
2605.10213 v1

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-12T04:23:05.827210Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+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

21 of 21 outbound references displayed

verified exact0
verified fuzzy21
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 718fcc65-35fb-409b-b941-241a11de6010 · outbound

This paper cites Model- driven deep learning for physical layer communications.

Unsupervised Online Channel Estimation for High-Mobility OFDM via Implicit Neural Representation Model- driven deep learning for physical layer communications

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T15:21:39.415205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d3d9252d-c0c4-4a74-ad4e-1f7436bd2f82 · outbound

This paper cites Adaptive and flexible model-based AI for deep receivers in dynamic channels.

Unsupervised Online Channel Estimation for High-Mobility OFDM via Implicit Neural Representation Adaptive and flexible model-based AI for deep receivers in dynamic channels

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T15:21:39.425031Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 4fa782ca-7f44-467e-a1c1-b59a87df92a0 · outbound

This paper cites Adaptive implicit-based deep learning channel estimation for 6G communications.

Unsupervised Online Channel Estimation for High-Mobility OFDM via Implicit Neural Representation Adaptive implicit-based deep learning channel estimation for 6G communications

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T15:21:39.420549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 2c5b4294-29c8-4594-a39f-0cccab9a0163 · outbound

This paper cites Learning the MMSE channel estimator.

Unsupervised Online Channel Estimation for High-Mobility OFDM via Implicit Neural Representation Learning the MMSE channel estimator

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T15:21:39.427100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 135ebc37-96b5-4b99-a8c6-d9bb9923cf76 · outbound

This paper cites DeepMMSE: A deep learning approach to MMSE-based noise power spectral density estimation.

Unsupervised Online Channel Estimation for High-Mobility OFDM via Implicit Neural Representation DeepMMSE: A deep learning approach to MMSE-based noise power spectral density estimation

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T15:21:39.404050Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 14687bc1-72d4-4d38-b8ea-466389ba6e25 · outbound

This paper cites Deep learning-based channel estimation.

Unsupervised Online Channel Estimation for High-Mobility OFDM via Implicit Neural Representation Deep learning-based channel estimation

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T15:21:39.438709Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 7c7789d3-c9b4-4e5f-990c-f98b4c04b328 · outbound

This paper cites Deep learning- assisted OFDM channel estimation and signal detection technology.

Unsupervised Online Channel Estimation for High-Mobility OFDM via Implicit Neural Representation Deep learning- assisted OFDM channel estimation and signal detection technology

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T15:21:39.418159Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation e824134f-e6e4-495c-87ca-ef92a6b1aa1e · outbound

This paper cites Deep learning based channel estimation in high mobility communications using Bi-RNN networks.

Unsupervised Online Channel Estimation for High-Mobility OFDM via Implicit Neural Representation Deep learning based channel estimation in high mobility communications using Bi-RNN networks

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T15:21:39.409518Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d155fbaf-e896-40e3-b5cb-9230f9bf8eaa · outbound

This paper cites Channel estimation and symbol demod- ulation for OFDM systems over rapidly varying multipath channels with hybrid deep neural networks.

Unsupervised Online Channel Estimation for High-Mobility OFDM via Implicit Neural Representation Channel estimation and symbol demod- ulation for OFDM systems over rapidly varying multipath channels with hybrid deep neural networks

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T15:21:39.412407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation ee2f88b8-0783-43ae-b53f-e1785dee0e69 · outbound

This paper cites Deep equilibrium models.

Unsupervised Online Channel Estimation for High-Mobility OFDM via Implicit Neural Representation Deep equilibrium models

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T15:21:39.440599Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c1a036ae-5dd9-4bab-af71-94087366b515 · outbound

This paper cites GSURE-based unsupervised deep equilibrium model learning for large-scale channel estimation.

Unsupervised Online Channel Estimation for High-Mobility OFDM via Implicit Neural Representation GSURE-based unsupervised deep equilibrium model learning for large-scale channel estimation

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T15:21:39.442813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 6295ed4e-460d-4d75-b9af-5a5a0d8b0818 · outbound

This paper cites Leveraging variational autoencoders for parameterized MMSE estimation.

Unsupervised Online Channel Estimation for High-Mobility OFDM via Implicit Neural Representation Leveraging variational autoencoders for parameterized MMSE estimation

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T15:21:39.398943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation e4313f3d-1320-4d4d-9d01-8e44e99c3026 · outbound

This paper cites Channel estimation in underdetermined systems utilizing variational autoencoders.

Unsupervised Online Channel Estimation for High-Mobility OFDM via Implicit Neural Representation Channel estimation in underdetermined systems utilizing variational autoencoders

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T15:21:39.445046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 640e730b-6fea-4ee6-bf99-a787cc03fa21 · outbound

This paper cites Unsupervised learning for ultra-reliable and low-latency communications with practical channel estimation.

Unsupervised Online Channel Estimation for High-Mobility OFDM via Implicit Neural Representation Unsupervised learning for ultra-reliable and low-latency communications with practical channel estimation

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T15:21:39.434467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 4d62f9d9-012e-4092-9262-fd42a81afdf7 · outbound

This paper cites C-GRBFnet: A physics-inspired generative deep neural network for channel representation and prediction.

Unsupervised Online Channel Estimation for High-Mobility OFDM via Implicit Neural Representation C-GRBFnet: A physics-inspired generative deep neural network for channel representation and prediction

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T15:21:39.407052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 7f3a8e7f-3d73-4970-b08b-1961e45ee11e · outbound

This paper cites Model- based learning for multi-antenna multi-frequency location-to-channel mapping.

Unsupervised Online Channel Estimation for High-Mobility OFDM via Implicit Neural Representation Model- based learning for multi-antenna multi-frequency location-to-channel mapping

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T15:21:39.429396Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3a1f21c7-777a-4bf7-b03a-61253215e20e · outbound

This paper cites Physics-informed implicit neural representation for wireless imaging in RIS-aided ISAC system.

Unsupervised Online Channel Estimation for High-Mobility OFDM via Implicit Neural Representation Physics-informed implicit neural representation for wireless imaging in RIS-aided ISAC system

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T15:21:39.431919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 86ac9f68-5b68-43e3-b70a-7de958ff6082 · outbound

This paper cites Robust channel estimation for OFDM systems with rapid dispersive fading channels.

Unsupervised Online Channel Estimation for High-Mobility OFDM via Implicit Neural Representation Robust channel estimation for OFDM systems with rapid dispersive fading channels

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T15:21:39.436868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 58bb64f9-78c9-4c32-be88-a38a9ca9082b · outbound

This paper cites Neural tangent kernel: Conver- gence and generalization in neural networks.

Unsupervised Online Channel Estimation for High-Mobility OFDM via Implicit Neural Representation Neural tangent kernel: Conver- gence and generalization in neural networks

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T15:21:39.396272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 569d9b31-ca46-4148-8aa7-a26249b42c92 · outbound

This paper cites Fourier features let networks learn high frequency functions in low dimensional domains.

Unsupervised Online Channel Estimation for High-Mobility OFDM via Implicit Neural Representation Fourier features let networks learn high frequency functions in low dimensional domains

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T15:21:39.401448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f4d31a85-9471-4931-88bd-02d7519e6e04 · outbound

This paper cites Study on channel model for frequencies from 0.5 to 100 GHz.

Unsupervised Online Channel Estimation for High-Mobility OFDM via Implicit Neural Representation Study on channel model for frequencies from 0.5 to 100 GHz

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T15:21:39.423078Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Pith citing papers

No inbound Pith citation observations are available.