Paper Citation Record · LEDGER

Environment-Aware Channel Prediction for Vehicular Communications: A Multimodal Visual Feature Fusion Framework

As of 4 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2604.02396.

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

pith.paper-citation-record.v1
2604.02396 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-13T21:57:14.108010Z

measured 34 of 34 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

34 of 34 outbound references displayed

verified exact2
verified fuzzy32
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 24fb85a8-8ea5-49b7-98da-dcfc9a8142a4 · outbound

This paper cites Environment- aware path loss prediction using panoramic images for vehicular com- munications.

Environment-Aware Channel Prediction for Vehicular Communications: A Multimodal Visual Feature Fusion Framework Environment- aware path loss prediction using panoramic images for vehicular com- munications

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T21:58:20.032257Z

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 8eee0e0d-6f24-4fb9-a6db-cd8f8f75c952 · outbound

This paper cites Framework and overall objectives of the future development of IMT for 2030 and beyond.

Environment-Aware Channel Prediction for Vehicular Communications: A Multimodal Visual Feature Fusion Framework Framework and overall objectives of the future development of IMT for 2030 and beyond

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T21:58:20.005596Z

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 e2a02521-9687-489e-bc1d-8a2d5f0fdb85 · outbound

This paper cites A general channel model for integrated sensing and communication scenarios.

Environment-Aware Channel Prediction for Vehicular Communications: A Multimodal Visual Feature Fusion Framework A general channel model for integrated sensing and communication scenarios

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T21:58:20.044757Z

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 c988f9b5-107a-46bb-92fc-21a9663786a8 · outbound

This paper cites COST CA20120 INTERACT framework of artificial intelligence-based channel modeling.

Environment-Aware Channel Prediction for Vehicular Communications: A Multimodal Visual Feature Fusion Framework COST CA20120 INTERACT framework of artificial intelligence-based channel modeling

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T21:58:20.010882Z

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 8e3d06cb-5cad-4d71-b7a0-e69956405f89 · outbound

This paper cites Propagation channels of 5G millimeter-wave vehicle-to- vehicle communications: Recent advances and future challenges.

Environment-Aware Channel Prediction for Vehicular Communications: A Multimodal Visual Feature Fusion Framework Propagation channels of 5G millimeter-wave vehicle-to- vehicle communications: Recent advances and future challenges

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T21:58:20.020020Z

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 780cbee7-51b0-414b-a355-8689745dafce · outbound

This paper cites Artificial intelligence empowered channel prediction: A new paradigm for propagation channel modeling.

Environment-Aware Channel Prediction for Vehicular Communications: A Multimodal Visual Feature Fusion Framework Artificial intelligence empowered channel prediction: A new paradigm for propagation channel modeling

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-13T21:58:19.807459Z

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 7f749844-ccb5-4654-8e10-7d785b8c965a · outbound

This paper cites Artificial intelligence enabled radio propagation for communications—Part I: Channel characterization and antenna-channel optimization.

Environment-Aware Channel Prediction for Vehicular Communications: A Multimodal Visual Feature Fusion Framework Artificial intelligence enabled radio propagation for communications—Part I: Channel characterization and antenna-channel optimization

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T21:58:20.018211Z

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 7ab18851-24dd-4499-b9c5-f20db1e9dbd5 · outbound

This paper cites Multi-modal intelligent channel modeling: A new modeling paradigm via synesthesia of machines.

Environment-Aware Channel Prediction for Vehicular Communications: A Multimodal Visual Feature Fusion Framework Multi-modal intelligent channel modeling: A new modeling paradigm via synesthesia of machines

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T21:58:20.016408Z

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 7e07a97a-8898-4e19-86dc-f16cf10b9a22 · outbound

This paper cites Applying deep-learning-based com- puter vision to wireless communications: Methodologies, opportunities, and challenges.

Environment-Aware Channel Prediction for Vehicular Communications: A Multimodal Visual Feature Fusion Framework Applying deep-learning-based com- puter vision to wireless communications: Methodologies, opportunities, and challenges

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T21:58:20.035685Z

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 00a9a853-80ec-433a-9be4-6aff79295da2 · outbound

This paper cites When wireless communications meet computer vision in beyond 5G.

Environment-Aware Channel Prediction for Vehicular Communications: A Multimodal Visual Feature Fusion Framework When wireless communications meet computer vision in beyond 5G

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T21:58:20.041340Z

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 cc588f2f-6dad-447e-97b9-7639495aa134 · outbound

This paper cites Vision-aided 6G wireless communications: Blockage prediction and proactive handoff.

Environment-Aware Channel Prediction for Vehicular Communications: A Multimodal Visual Feature Fusion Framework Vision-aided 6G wireless communications: Blockage prediction and proactive handoff

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T21:58:20.048164Z

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 d6d49c74-6dab-495b-ad9f-4666ffabc430 · outbound

This paper cites Environment semantics aided wireless communications: A case study of mmWave beam pre- diction and blockage prediction.

Environment-Aware Channel Prediction for Vehicular Communications: A Multimodal Visual Feature Fusion Framework Environment semantics aided wireless communications: A case study of mmWave beam pre- diction and blockage prediction

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T21:58:20.012737Z

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 5013d4aa-32b1-4685-a963-bb6ee04e49e9 · outbound

This paper cites Environment semantic com- munication: Enabling distributed sensing aided networks.

Environment-Aware Channel Prediction for Vehicular Communications: A Multimodal Visual Feature Fusion Framework Environment semantic com- munication: Enabling distributed sensing aided networks

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T21:58:20.003670Z

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 be632615-5b7b-40c4-a4ff-f325610bc531 · outbound

This paper cites Camera based mmWave beam prediction: Towards multi-candidate real-world scenarios.

Environment-Aware Channel Prediction for Vehicular Communications: A Multimodal Visual Feature Fusion Framework Camera based mmWave beam prediction: Towards multi-candidate real-world scenarios

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T21:58:20.043101Z

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 93986ff2-148c-442b-98ac-a41e0b3f9ed9 · outbound

This paper cites Deepsense 6G: A large-scale real-world multi-modal sensing and communication dataset.

Environment-Aware Channel Prediction for Vehicular Communications: A Multimodal Visual Feature Fusion Framework Deepsense 6G: A large-scale real-world multi-modal sensing and communication dataset

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T21:58:20.033975Z

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 2e198144-89dd-40f6-99b1-6150f146561a · outbound

This paper cites Environment sensing- aided beam prediction with transfer learning for smart factory.

Environment-Aware Channel Prediction for Vehicular Communications: A Multimodal Visual Feature Fusion Framework Environment sensing- aided beam prediction with transfer learning for smart factory

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T21:58:19.998281Z

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 1ede98ec-1614-4c58-b1ea-a9fab6f9f92f · outbound

This paper cites Proactive received power prediction using machine learning and depth images for mmWave networks.

Environment-Aware Channel Prediction for Vehicular Communications: A Multimodal Visual Feature Fusion Framework Proactive received power prediction using machine learning and depth images for mmWave networks

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T21:58:20.037393Z

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 d7ca2684-2f3b-489d-a481-85c4a3442e1b · outbound

This paper cites Vision aided channel prediction for vehicular communications: A case study of received power prediction using RGB images.

Environment-Aware Channel Prediction for Vehicular Communications: A Multimodal Visual Feature Fusion Framework Vision aided channel prediction for vehicular communications: A case study of received power prediction using RGB images

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T21:58:20.046446Z

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 fae93fab-b65f-4783-96ac-a15402eac4b0 · outbound

This paper cites Multi- modal environmental information sensing based path loss prediction for V2I communications.

Environment-Aware Channel Prediction for Vehicular Communications: A Multimodal Visual Feature Fusion Framework Multi- modal environmental information sensing based path loss prediction for V2I communications

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T21:58:20.051676Z

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 42a08bb4-a042-4e6e-a81f-682ba5beb0b5 · outbound

This paper cites An intelligent path loss prediction approach based on integrated sensing and communi- cations for future vehicular networks.

Environment-Aware Channel Prediction for Vehicular Communications: A Multimodal Visual Feature Fusion Framework An intelligent path loss prediction approach based on integrated sensing and communi- cations for future vehicular networks

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T21:58:20.049826Z

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 ef1a2666-0b91-45df-9d25-c9db384b517d · outbound

This paper cites Path loss pre- diction for vehicle-to-infrastructure communications via synesthesia of machines (SoM).

Environment-Aware Channel Prediction for Vehicular Communications: A Multimodal Visual Feature Fusion Framework Path loss pre- diction for vehicle-to-infrastructure communications via synesthesia of machines (SoM)

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T21:58:20.007464Z

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 42829020-c5ee-4cde-80ec-51a9cf92d58a · outbound

This paper cites Multi-modal sensing data- based real-time path loss prediction for 6G UA V-to-ground communi- cations.

Environment-Aware Channel Prediction for Vehicular Communications: A Multimodal Visual Feature Fusion Framework Multi-modal sensing data- based real-time path loss prediction for 6G UA V-to-ground communi- cations

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T21:58:20.026984Z

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 c105875d-b866-4afd-8b2c-0faa47a0b624 · outbound

This paper cites Vision-aided channel prediction based on image segmentation at street intersection scenarios.

Environment-Aware Channel Prediction for Vehicular Communications: A Multimodal Visual Feature Fusion Framework Vision-aided channel prediction based on image segmentation at street intersection scenarios

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T21:58:20.039174Z

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 36a7f104-8c48-4bd6-ab63-81f2df0809fe · outbound

This paper cites A multimodal predictive channel model based on dual-camera images for IIoT communications.

Environment-Aware Channel Prediction for Vehicular Communications: A Multimodal Visual Feature Fusion Framework A multimodal predictive channel model based on dual-camera images for IIoT communications

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T21:58:20.028940Z

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 e8d01a0d-a854-4002-83cb-6b419bfcb203 · outbound

This paper cites Multimodal fusion-based channel prediction and characterization for mmWave UA V A2G communications.

Environment-Aware Channel Prediction for Vehicular Communications: A Multimodal Visual Feature Fusion Framework Multimodal fusion-based channel prediction and characterization for mmWave UA V A2G communications

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T21:58:20.000117Z

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 8fc1ceec-5bc2-42ac-9b0b-1a74a570889b · outbound

This paper cites A cluster-based predictive channel modeling for mmwave communications via deep transfer learning: A multimodal data-driven approach.

Environment-Aware Channel Prediction for Vehicular Communications: A Multimodal Visual Feature Fusion Framework A cluster-based predictive channel modeling for mmwave communications via deep transfer learning: A multimodal data-driven approach

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T21:58:20.014459Z

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 c45a4262-85a3-4955-860e-9331ab9f3005 · outbound

This paper cites M3SC: A generic dataset for mixed multi-modal (MMM) sensing and communication integration.

Environment-Aware Channel Prediction for Vehicular Communications: A Multimodal Visual Feature Fusion Framework M3SC: A generic dataset for mixed multi-modal (MMM) sensing and communication integration

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T21:58:20.023532Z

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 f7c47918-cd98-41fd-aa41-3a4397a1b5f5 · outbound

This paper cites Deep residual learning for image recognition.

Environment-Aware Channel Prediction for Vehicular Communications: A Multimodal Visual Feature Fusion Framework Deep residual learning for image recognition

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T21:58:20.001942Z

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 ed8d9c4d-692b-4f48-af61-9fa71f5d6871 · outbound

This paper cites Novel scalable MIMO chan- nel sounding technique and measurement accuracy evaluation with transceiver impairments.

Environment-Aware Channel Prediction for Vehicular Communications: A Multimodal Visual Feature Fusion Framework Novel scalable MIMO chan- nel sounding technique and measurement accuracy evaluation with transceiver impairments

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T21:58:20.030576Z

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 9f804682-cf5b-496d-971a-64b2dd387761 · outbound

This paper cites Urban Macro/Microcellular Channel Characterization at 4.85~GHz With Literature-Referenced Upper-FR1-to-FR3 Cross-Band Analysis.

Environment-Aware Channel Prediction for Vehicular Communications: A Multimodal Visual Feature Fusion Framework Urban Macro/Microcellular Channel Characterization at 4.85~GHz With Literature-Referenced Upper-FR1-to-FR3 Cross-Band Analysis

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-05-13T21:58:19.810183Z

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 0a3c7361-80f7-403e-97d4-f643106741cd · outbound

This paper cites Masked-attention mask transformer for universal image segmentation.

Environment-Aware Channel Prediction for Vehicular Communications: A Multimodal Visual Feature Fusion Framework Masked-attention mask transformer for universal image segmentation

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T21:58:19.996144Z

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 f8762f6a-93ca-4b50-95fb-233fe80467e7 · outbound

This paper cites Depth anything v2.

Environment-Aware Channel Prediction for Vehicular Communications: A Multimodal Visual Feature Fusion Framework Depth anything v2

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T21:58:20.009027Z

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 b94c1ad5-b971-4888-8cd6-0bc574185818 · outbound

This paper cites The cityscapes dataset for semantic urban scene understanding.

Environment-Aware Channel Prediction for Vehicular Communications: A Multimodal Visual Feature Fusion Framework The cityscapes dataset for semantic urban scene understanding

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T21:58:20.025260Z

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 4cababa1-1491-4d9f-bfc8-bdf5916f9d8e · outbound

This paper cites Imagenet large scale visual recognition chal- lenge.

Environment-Aware Channel Prediction for Vehicular Communications: A Multimodal Visual Feature Fusion Framework Imagenet large scale visual recognition chal- lenge

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T21:58:20.021669Z

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.