Paper Citation Record · LEDGER

Deep Learning-Based Site-Specific Channel Modeling and Inference

As of 4 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 1 inbound Pith citation observation for arXiv:2603.28083.

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

pith.paper-citation-record.v1
2603.28083 v2

Coverage vector

measured 54 of 54 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-14T01:59:24.065028Z

measured 55 of 55 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 2026-06-28T05:09:07.754702Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-02T10:16:52.312961Z

Reference resolution

54 of 54 outbound references displayed

verified exact5
verified fuzzy48
unresolved0
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c1d18919-3fd3-432a-9557-9a3728bc3b98 · outbound

This paper cites Site-specific radio channel representation for 5G and 6G.

Deep Learning-Based Site-Specific Channel Modeling and Inference Site-specific radio channel representation for 5G and 6G

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:03:39.923031Z

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 8dbf6b3e-8af2-4b87-b343-f7817cadab93 · outbound

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

Deep Learning-Based Site-Specific Channel Modeling and Inference Propagation channels of 5G millimeter-wave vehicle-to-vehicle vommunications: recent advances and future challenges

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:03:39.951082Z

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 2e60348c-1418-4058-9aca-8403d996750f · outbound

This paper cites Aber performance evaluation of RIS-Aided millimeter wave massive MIMO system under 3GPP 5G channels.

Deep Learning-Based Site-Specific Channel Modeling and Inference Aber performance evaluation of RIS-Aided millimeter wave massive MIMO system under 3GPP 5G channels

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:03:39.851552Z

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 4b2f2970-6256-4090-a323-3fc8fd5ec55a · outbound

This paper cites Analytical channel modeling: From MIMO to extra large-scale MIMO.

Deep Learning-Based Site-Specific Channel Modeling and Inference Analytical channel modeling: From MIMO to extra large-scale MIMO

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:03:39.873913Z

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 4db8e570-7ac2-4993-a866-437ba1e55b47 · outbound

This paper cites He and B.

Deep Learning-Based Site-Specific Channel Modeling and Inference He and B

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:03:39.877542Z

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 4aad2e60-6aa4-4014-96ad-7015c1773825 · outbound

This paper cites Characterization of quasi-stationarity regions for vehicle- to-vehicle radio channels.

Deep Learning-Based Site-Specific Channel Modeling and Inference Characterization of quasi-stationarity regions for vehicle- to-vehicle radio channels

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:03:39.838897Z

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 2645bdca-b18a-46f8-b9ad-ab8c748de3ca · outbound

This paper cites Non-stationarity characteristics in dynamic vehicular isac channels at 28 GHz.

Deep Learning-Based Site-Specific Channel Modeling and Inference Non-stationarity characteristics in dynamic vehicular isac channels at 28 GHz

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:03:39.831602Z

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 d1efb99a-3d5c-4bea-a16f-416db4994411 · outbound

This paper cites 3GPP TR 38.901 channel model.

Deep Learning-Based Site-Specific Channel Modeling and Inference 3GPP TR 38.901 channel model

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:03:39.847810Z

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 65ffa21a-d274-4b46-a290-19c93a38e927 · outbound

This paper cites WINNER II channel models.

Deep Learning-Based Site-Specific Channel Modeling and Inference WINNER II channel models

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:03:39.854117Z

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 923b0a9f-c398-4571-9943-c5bb0143f4b6 · outbound

This paper cites The COST 2100 MIMO channel model.

Deep Learning-Based Site-Specific Channel Modeling and Inference The COST 2100 MIMO channel model

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:03:39.828029Z

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 89665a99-ea7c-4295-a27b-824a9e31a237 · outbound

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

Deep Learning-Based Site-Specific Channel Modeling and Inference COST CA20120 interact framework of artificial intelligence-based channel modeling

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:03:39.857192Z

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 be2a66a0-e140-45d0-9ebd-2eebdc3d461c · outbound

This paper cites METIS Channel Models (D1.4).

Deep Learning-Based Site-Specific Channel Modeling and Inference METIS Channel Models (D1.4)

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:03:39.978406Z

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 36ccae9b-900f-4a78-899a-da06c5759148 · outbound

This paper cites Generating MIMO channels for 6G virtual worlds using ray-tracing simulations.

Deep Learning-Based Site-Specific Channel Modeling and Inference Generating MIMO channels for 6G virtual worlds using ray-tracing simulations

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:03:39.941134Z

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 e484440b-9b35-4e19-8f6b-8c5c29fe1d73 · outbound

This paper cites Accurate urban path loss models including diffuse scatter.

Deep Learning-Based Site-Specific Channel Modeling and Inference Accurate urban path loss models including diffuse scatter

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:03:39.902114Z

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 2e069df6-fdf7-4797-823d-b4c17446f467 · outbound

This paper cites Around-corner and over-top 28 GHz measurement in manhattan: Path loss and AoA for MU-MIMO.

Deep Learning-Based Site-Specific Channel Modeling and Inference Around-corner and over-top 28 GHz measurement in manhattan: Path loss and AoA for MU-MIMO

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:03:39.888297Z

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 6273216c-a5c3-47d9-913d-8c24b0de3cd3 · outbound

This paper cites Non-geometrical stochastic model for non-stationary wideband vehicular communication channels.

Deep Learning-Based Site-Specific Channel Modeling and Inference Non-geometrical stochastic model for non-stationary wideband vehicular communication channels

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:03:39.891469Z

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 79941c2c-6aa9-436e-ba9b-d91be978eca0 · outbound

This paper cites Non-stationary time-varying vehicular channel characteristics for different roadside scattering environments.

Deep Learning-Based Site-Specific Channel Modeling and Inference Non-stationary time-varying vehicular channel characteristics for different roadside scattering environments

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:03:39.895204Z

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 5a44580d-6e85-412d-8ae6-c69724360707 · outbound

This paper cites Characterization of quasi-stationarity regions for V2V channels in various driving states.

Deep Learning-Based Site-Specific Channel Modeling and Inference Characterization of quasi-stationarity regions for V2V channels in various driving states

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:03:39.911808Z

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 1eb1a9f9-d394-4d21-8980-993941cfe865 · outbound

This paper cites Dynamic V2V channel measurement and modeling at street intersection scenarios.

Deep Learning-Based Site-Specific Channel Modeling and Inference Dynamic V2V channel measurement and modeling at street intersection scenarios

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:03:39.919579Z

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 3cef5906-4354-412b-ba8c-a30bfd6f9da7 · outbound

This paper cites Double-Directional V2V Channel Measurement using ReRoMA at 60 GHz.

Deep Learning-Based Site-Specific Channel Modeling and Inference Double-Directional V2V Channel Measurement using ReRoMA at 60 GHz

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-14T02:03:37.923171Z

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 b80ae4ac-8334-4ac8-a669-af2390e706de · outbound

This paper cites Measurement based tapped delay line model for train-to-train communications.

Deep Learning-Based Site-Specific Channel Modeling and Inference Measurement based tapped delay line model for train-to-train communications

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:03:39.898690Z

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 b55dc4fe-ed05-4b0e-abb1-73b524ad4d26 · outbound

This paper cites Autoregressive modeling approach for non-stationary vehicular channel simulation.

Deep Learning-Based Site-Specific Channel Modeling and Inference Autoregressive modeling approach for non-stationary vehicular channel simulation

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:03:39.988821Z

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 ae933469-5753-4b0d-aaaf-598e92b37d8e · outbound

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

Deep Learning-Based Site-Specific Channel Modeling and Inference Artificial intelligence empowered channel prediction: A new paradigm for propagation channel modeling

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-14T02:03:37.946679Z

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 42a85e40-2a04-43f4-a697-98b9db189a79 · outbound

This paper cites Artificial intelligence enabled radio propagation for communications—part II: Scenario identification and channel modeling.

Deep Learning-Based Site-Specific Channel Modeling and Inference Artificial intelligence enabled radio propagation for communications—part II: Scenario identification and channel modeling

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:03:39.820928Z

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 8adeddc4-39a0-4a1a-9a00-61c4068bb37b · outbound

This paper cites Generative adversarial networks based digital twin channel modeling for intelligent communication networks.

Deep Learning-Based Site-Specific Channel Modeling and Inference Generative adversarial networks based digital twin channel modeling for intelligent communication networks

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:03:39.817532Z

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 7c063c42-abbc-4339-ad8d-6715f51eb2ea · outbound

This paper cites Path loss prediction based on machine learning techniques: Principal component analysis, artificial neural network, and gaussian process.

Deep Learning-Based Site-Specific Channel Modeling and Inference Path loss prediction based on machine learning techniques: Principal component analysis, artificial neural network, and gaussian process

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:03:39.824637Z

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 d1eb40aa-a7aa-48c0-a5d4-ac9e280c149b · outbound

This paper cites Available: https://www.mdpi.com/1424-8220/20/7/1927.

Deep Learning-Based Site-Specific Channel Modeling and Inference Available: https://www.mdpi.com/1424-8220/20/7/1927

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:03:39.810378Z

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 da784dd4-6237-4e75-b00f-ea266d4f9555 · outbound

This paper cites Path loss modeling: A machine learning based approach using support vector regression and radial basis function models.

Deep Learning-Based Site-Specific Channel Modeling and Inference Path loss modeling: A machine learning based approach using support vector regression and radial basis function models

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:03:39.807164Z

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 edd5f4e0-88fb-42a6-931b-4af6f94c4271 · outbound

This paper cites Machine learning-based path loss model- ing with simplified features.

Deep Learning-Based Site-Specific Channel Modeling and Inference Machine learning-based path loss model- ing with simplified features

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:03:39.813853Z

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 69b966f1-5f7f-47f3-aa45-8433c4edf5b3 · outbound

This paper cites Path loss prediction using machine learning with extended features.

Deep Learning-Based Site-Specific Channel Modeling and Inference Path loss prediction using machine learning with extended features

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:03:39.835222Z

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 7c00944b-c46c-46dc-8f77-776afd9ca6d2 · outbound

This paper cites Machine learning for improved path loss prediction in urban vehicle-to-infrastructure communication systems.

Deep Learning-Based Site-Specific Channel Modeling and Inference Machine learning for improved path loss prediction in urban vehicle-to-infrastructure communication systems

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-14T02:03:37.511461Z

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 a45dea03-8378-4873-a9a4-74d7933122f4 · outbound

This paper cites Structural 3D reconstruction of indoor space for 5G signal simulation with mobile laser scanning point clouds.

Deep Learning-Based Site-Specific Channel Modeling and Inference Structural 3D reconstruction of indoor space for 5G signal simulation with mobile laser scanning point clouds

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:03:39.792597Z

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 ee922bdc-6162-4069-aa72-7e711258b232 · outbound

This paper cites Interpretable AI-based large-scale 3D pathloss prediction model for enabling emerg- ing self-driving networks.

Deep Learning-Based Site-Specific Channel Modeling and Inference Interpretable AI-based large-scale 3D pathloss prediction model for enabling emerg- ing self-driving networks

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:03:39.868140Z

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 4e461d28-28fe-47d7-bd55-b0fefb1fafb0 · outbound

This paper cites Non-stationary UA V A2G channel characterization: From urban mea- surements to expert-assisted neural modeling.

Deep Learning-Based Site-Specific Channel Modeling and Inference Non-stationary UA V A2G channel characterization: From urban mea- surements to expert-assisted neural modeling

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:03:39.803690Z

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 907f4708-9ea9-467e-bb14-fb529b8d0d09 · outbound

This paper cites MARS: Radio Map Super-resolution and Reconstruction Method under Sparse Channel Measurements.

Deep Learning-Based Site-Specific Channel Modeling and Inference MARS: Radio Map Super-resolution and Reconstruction Method under Sparse Channel Measurements

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-14T02:03:37.930440Z

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 0d215de2-5313-41a6-a435-d4b280779018 · outbound

This paper cites Millimeter wave base stations with cameras: Vision-aided beam and blockage prediction.

Deep Learning-Based Site-Specific Channel Modeling and Inference Millimeter wave base stations with cameras: Vision-aided beam and blockage prediction

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:03:39.884667Z

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 b8418f00-d265-4d03-8267-fdafab350a17 · outbound

This paper cites Blockage prediction in an outdoor mmwave environment by machine learning employing a top view image.

Deep Learning-Based Site-Specific Channel Modeling and Inference Blockage prediction in an outdoor mmwave environment by machine learning employing a top view image

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:03:39.781827Z

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 0a3da2e4-f3d4-47f3-b5ad-51efc7f20616 · outbound

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

Deep Learning-Based Site-Specific Channel Modeling and Inference Vision aided channel prediction for vehicular communications: A case study of received power prediction using RGB images

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:03:39.785081Z

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 b0f9ce73-ed09-4d17-b0f5-7eb201d4716a · outbound

This paper cites CNN-Based path loss prediction with enhanced satellite images.

Deep Learning-Based Site-Specific Channel Modeling and Inference CNN-Based path loss prediction with enhanced satellite images

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:03:39.788634Z

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 158332dd-8404-4b36-8342-e3353d135d0d · outbound

This paper cites DL- Enhanced channel parameter prediction scheme based on adaptive meta mask R-CNN model.

Deep Learning-Based Site-Specific Channel Modeling and Inference DL- Enhanced channel parameter prediction scheme based on adaptive meta mask R-CNN model

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:03:39.796409Z

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 1a06def2-f46a-4e8a-bcc6-648bd61896e2 · outbound

This paper cites Deep learning-based path loss prediction with satellite maps.

Deep Learning-Based Site-Specific Channel Modeling and Inference Deep learning-based path loss prediction with satellite maps

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:03:39.775494Z

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 a4246045-9864-40da-aea8-a6957b2b9aa4 · outbound

This paper cites Channel path loss prediction using satellite images: A deep learning approach.

Deep Learning-Based Site-Specific Channel Modeling and Inference Channel path loss prediction using satellite images: A deep learning approach

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:03:39.778947Z

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 aefa2989-5a6c-48cf-b90a-c196c611af88 · outbound

This paper cites A hybrid model-assisted approach for path loss prediction in suburban scenarios.

Deep Learning-Based Site-Specific Channel Modeling and Inference A hybrid model-assisted approach for path loss prediction in suburban scenarios

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-05-14T02:03:37.937658Z

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 8aebd568-2813-45ef-b5f0-b7c44d24227f · outbound

This paper cites Accurate path loss prediction using a neural network ensemble method.

Deep Learning-Based Site-Specific Channel Modeling and Inference Accurate path loss prediction using a neural network ensemble method

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:03:39.766374Z

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 f5884090-9162-4013-9f15-e3285828a67b · outbound

This paper cites Deep learning method for path loss prediction in mobile communication sys- tems.

Deep Learning-Based Site-Specific Channel Modeling and Inference Deep learning method for path loss prediction in mobile communication sys- tems

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:03:39.771977Z

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 1df51b58-2521-4caf-bff2-7da7c5b68b7e · outbound

This paper cites Improving path loss predic- tion using environmental feature extraction from satellite images: Hand- crafted vs. convolutional neural network.

Deep Learning-Based Site-Specific Channel Modeling and Inference Improving path loss predic- tion using environmental feature extraction from satellite images: Hand- crafted vs. convolutional neural network

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:03:39.800162Z

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 86031245-0a29-44a2-b60f-a2f89b6c31de · outbound

This paper cites An ubiquitous 2.6 GHz radio propagation model for wireless networks using self- supervised learning from satellite images.

Deep Learning-Based Site-Specific Channel Modeling and Inference An ubiquitous 2.6 GHz radio propagation model for wireless networks using self- supervised learning from satellite images

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:03:39.843606Z

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 ac3ade0c-9bc7-4a19-9d0b-eab5eaf73c8b · outbound

This paper cites Radio map prediction from aerial images and application to coverage optimization.

Deep Learning-Based Site-Specific Channel Modeling and Inference Radio map prediction from aerial images and application to coverage optimization

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:03:39.881207Z

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 2a125c3f-8bc4-44c5-93bb-d63d7202bd52 · outbound

This paper cites Model-aided deep learning method for path loss prediction in mobile communication systems at 2.6 GHz.

Deep Learning-Based Site-Specific Channel Modeling and Inference Model-aided deep learning method for path loss prediction in mobile communication systems at 2.6 GHz

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:03:39.966423Z

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Observation ffadffd0-5732-4c72-8d27-a5918a65dac9 · outbound

This paper cites Drive test minimization using deep learning with bayesian approximation.

Deep Learning-Based Site-Specific Channel Modeling and Inference Drive test minimization using deep learning with bayesian approximation

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:03:39.959249Z

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Observation 727fa775-fa9d-4f86-89d6-8ff11e0a2843 · outbound

This paper cites A deep-learning method for path loss predic- tion using geospatial information and path profiles.

Deep Learning-Based Site-Specific Channel Modeling and Inference A deep-learning method for path loss predic- tion using geospatial information and path profiles

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:03:39.973494Z

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Observation 43e75e33-69bf-4e3f-882e-bfa3373c25e4 · outbound

This paper cites Deep learning and fresnel zone theory for enhanced channel prediction using adaptive image features.

Deep Learning-Based Site-Specific Channel Modeling and Inference Deep learning and fresnel zone theory for enhanced channel prediction using adaptive image features

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:03:39.916027Z

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Observation 08129cbc-2a39-41fb-9b77-6f997a9cd484 · outbound

This paper cites Segformer: Simple and efficient design for semantic segmentation with transformers.

Deep Learning-Based Site-Specific Channel Modeling and Inference Segformer: Simple and efficient design for semantic segmentation with transformers

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:03:39.945044Z

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Observation 38f039e7-36d5-4ae5-a994-096e7e21bcc7 · outbound

This paper cites Naval Research Logistics Quarterly2(1–2), 83–97 (1955) https://doi.org/10.1002/nav.

Deep Learning-Based Site-Specific Channel Modeling and Inference Naval Research Logistics Quarterly2(1–2), 83–97 (1955) https://doi.org/10.1002/nav

Reference 54

Resolution: malformed identifier; doi_truncated, observed 2026-05-14T02:03:37.524506Z

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Pith citing papers

Observation 0149da11-9f5f-44e6-9dd9-494912c8537c · inbound

WiSER: A Wireless Scene Encoder for Geometry-Grounded Multi-View Wireless Prediction cites this paper.

WiSER: A Wireless Scene Encoder for Geometry-Grounded Multi-View Wireless Prediction Deep Learning-Based Site-Specific Channel Modeling and Inference

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-07-02T10:16:52.314257Z

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