Paper Citation Record · LEDGER

Geometry-Aided Channel Deduction: A Robust Channel Acquisition Framework Utilizing Coarse Scenario Prompt

As of 5 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 1 inbound Pith citation observation for arXiv:2605.07425.

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

pith.paper-citation-record.v1
2605.07425 v1

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-11T02:04:45.708791Z

measured 13 of 13 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-26T11:24:31.584556Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-04T08:39:41.580756Z

Reference resolution

12 of 12 outbound references displayed

verified exact3
verified fuzzy9
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 19ecc637-a35e-44eb-b87a-5dd832264133 · outbound

This paper cites Channel mapping based on in- terleaved learning with complex-domain MLP-mixer.

Geometry-Aided Channel Deduction: A Robust Channel Acquisition Framework Utilizing Coarse Scenario Prompt Channel mapping based on in- terleaved learning with complex-domain MLP-mixer

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T14:26:23.800782Z

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 4f02e42f-fe9b-4dd8-9194-c64f7c77ce7d · outbound

This paper cites Accurate channel prediction based on transformer: Making mobility negligible.

Geometry-Aided Channel Deduction: A Robust Channel Acquisition Framework Utilizing Coarse Scenario Prompt Accurate channel prediction based on transformer: Making mobility negligible

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T14:26:23.798100Z

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 7cfbd275-9bff-40eb-9c6b-e2c8bdc798aa · outbound

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

Geometry-Aided Channel Deduction: A Robust Channel Acquisition Framework Utilizing Coarse Scenario Prompt C-GRBFnet: A physics-inspired generative deep neural network for channel representation and predic- tion

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T14:26:23.788328Z

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 8379da38-bc44-46a7-b43d-51f73bf2dbd8 · outbound

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

Geometry-Aided Channel Deduction: A Robust Channel Acquisition Framework Utilizing Coarse Scenario Prompt Model-based learning for multi-antenna multi-frequency location-to-channel mapping

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T14:26:23.780722Z

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 f6330b3a-920d-4a59-a68d-17e8ac6084f2 · outbound

This paper cites Learning radio en- vironments by differentiable ray tracing.

Geometry-Aided Channel Deduction: A Robust Channel Acquisition Framework Utilizing Coarse Scenario Prompt Learning radio en- vironments by differentiable ray tracing

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T14:26:23.785527Z

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 3b2c0664-1c81-4b07-8f59-869a1f467378 · outbound

This paper cites Spatial channel deduction: Acquiring channel from approximate position and coarse estimate.

Geometry-Aided Channel Deduction: A Robust Channel Acquisition Framework Utilizing Coarse Scenario Prompt Spatial channel deduction: Acquiring channel from approximate position and coarse estimate

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T14:26:23.783132Z

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 1e331713-008a-44e8-ba89-b848178413c1 · outbound

This paper cites Channel deduction: A new learn- ing framework to acquire channel from outdated samples and coarse estimate.

Geometry-Aided Channel Deduction: A Robust Channel Acquisition Framework Utilizing Coarse Scenario Prompt Channel deduction: A new learn- ing framework to acquire channel from outdated samples and coarse estimate

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T14:26:23.790782Z

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 356ddbfd-ec24-4ba3-b154-b461b6579b7f · outbound

This paper cites Digital Twin Channel-Aided CSI Prediction: An Environment-Based Subspace Extraction Approach for Achieving Low Overhead and High Robustness.

Geometry-Aided Channel Deduction: A Robust Channel Acquisition Framework Utilizing Coarse Scenario Prompt Digital Twin Channel-Aided CSI Prediction: An Environment-Based Subspace Extraction Approach for Achieving Low Overhead and High Robustness

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-11T04:00:54.136006Z

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 6c907823-c05b-49ed-996e-29ff77218330 · outbound

This paper cites Can wireless environment informa- tion decrease pilot overhead: A channel prediction example.

Geometry-Aided Channel Deduction: A Robust Channel Acquisition Framework Utilizing Coarse Scenario Prompt Can wireless environment informa- tion decrease pilot overhead: A channel prediction example

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T14:26:23.793144Z

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 9c4579f2-7f98-4bb6-bce0-9acbba6c513e · outbound

This paper cites Analogical Learning for Cross-Scenario Generalization: Framework and Application to Intelligent Localization.

Geometry-Aided Channel Deduction: A Robust Channel Acquisition Framework Utilizing Coarse Scenario Prompt Analogical Learning for Cross-Scenario Generalization: Framework and Application to Intelligent Localization

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-11T04:00:54.127019Z

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 28642603-f519-4582-a7ab-54855f99ee06 · outbound

This paper cites Retrieval-Augmented Generation for Large Language Models: A Survey.

Geometry-Aided Channel Deduction: A Robust Channel Acquisition Framework Utilizing Coarse Scenario Prompt Retrieval-Augmented Generation for Large Language Models: A Survey

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-05-11T04:00:54.114095Z

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 52b788ab-03ce-4e9d-b2c0-b68d6e72cf17 · outbound

This paper cites Towards wireless native big AI model: The mission and approach differ from large language model.

Geometry-Aided Channel Deduction: A Robust Channel Acquisition Framework Utilizing Coarse Scenario Prompt Towards wireless native big AI model: The mission and approach differ from large language model

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T14:26:23.795541Z

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

Observation 8250721f-e883-4de6-b4c2-9631561b2341 · inbound

Full-Domain Coupler: A Wireless Native Neural Backbone for Channel Representation and Deduction cites this paper.

Full-Domain Coupler: A Wireless Native Neural Backbone for Channel Representation and Deduction Geometry-Aided Channel Deduction: A Robust Channel Acquisition Framework Utilizing Coarse Scenario Prompt

Reference 46

Resolution: verified exact; local_arxiv, observed 2026-07-04T08:39:41.582281Z

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.