Paper Citation Record · LEDGER

Enhancing Wireless Networks for IoT with Large Vision Models: Foundations and Applications

As of 14 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2508.00583.

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

pith.paper-citation-record.v1
2508.00583 v1

Coverage vector

measured 15 of 15 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T10:06:14.035177Z

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+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

15 of 15 outbound references displayed

verified exact1
verified fuzzy10
unresolved4
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 25e9ca08-8f00-444e-b8f9-c7374e8f6c97 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Enhancing Wireless Networks for IoT with Large Vision Models: Foundations and Applications An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T10:06:13.935080Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b564acf3-cc60-46a3-bdb7-fa1a2c636c45 · outbound

This paper cites Federated learning over fully-decoupled ran architecture for two-tier computing acceleration,.

Enhancing Wireless Networks for IoT with Large Vision Models: Foundations and Applications Federated learning over fully-decoupled ran architecture for two-tier computing acceleration,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:06:14.491025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 66f8f5fe-7be5-42b1-9b93-95c19c228f66 · outbound

This paper cites Embodied ai-enhanced vehicular networks: An in- tegrated vision language models and reinforcement learning method,.

Enhancing Wireless Networks for IoT with Large Vision Models: Foundations and Applications Embodied ai-enhanced vehicular networks: An in- tegrated vision language models and reinforcement learning method,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:06:14.449299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9625c54e-6787-4447-8e36-ad015d32b690 · outbound

This paper cites Millimeter wave drones with cameras: Computer vision aided wireless beam prediction,.

Enhancing Wireless Networks for IoT with Large Vision Models: Foundations and Applications Millimeter wave drones with cameras: Computer vision aided wireless beam prediction,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:06:14.417636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5ffa1025-c933-45f4-bda9-2d1b764a115a · outbound

This paper cites When Vision Transformers Outperform ResNets without Pre-training or Strong Data Augmentations.

Enhancing Wireless Networks for IoT with Large Vision Models: Foundations and Applications When Vision Transformers Outperform ResNets without Pre-training or Strong Data Augmentations

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T10:06:13.965784Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c78161a3-fff1-4212-b694-dd1b9a105bd8 · outbound

This paper cites Vision Aided Channel Prediction for Vehicular Communications: A Case Study of Received Power Prediction Using RGB Images.

Enhancing Wireless Networks for IoT with Large Vision Models: Foundations and Applications Vision Aided Channel Prediction for Vehicular Communications: A Case Study of Received Power Prediction Using RGB Images

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-08-06T10:06:14.155970Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3d80ca85-6e27-412c-ad90-71173c464eb6 · outbound

This paper cites Machine learning on camera images for fast mmwave beamforming,.

Enhancing Wireless Networks for IoT with Large Vision Models: Foundations and Applications Machine learning on camera images for fast mmwave beamforming,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:06:14.392202Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 66bca93b-ba31-4597-af4c-3669bce52dc8 · outbound

This paper cites MoE-AMC: Enhancing Automatic Modulation Classification Performance Using Mixture-of-Experts.

Enhancing Wireless Networks for IoT with Large Vision Models: Foundations and Applications MoE-AMC: Enhancing Automatic Modulation Classification Performance Using Mixture-of-Experts

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T10:06:13.989753Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c2ef6cf8-6347-488b-a8aa-07a85741f153 · outbound

This paper cites Applying deep-learning-based computer vision to wire- less communications: Methodologies, opportunities, and challenges,.

Enhancing Wireless Networks for IoT with Large Vision Models: Foundations and Applications Applying deep-learning-based computer vision to wire- less communications: Methodologies, opportunities, and challenges,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:06:14.357089Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4975607d-08d2-41a6-8cd6-8dca5e00211c · outbound

This paper cites Real-time anomaly detection of network traffic based on cnn,.

Enhancing Wireless Networks for IoT with Large Vision Models: Foundations and Applications Real-time anomaly detection of network traffic based on cnn,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:06:14.328307Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5aec7476-ce54-454c-a011-4c036dade738 · outbound

This paper cites Vision Transformer-based Semantic Communications With Importance-Aware Quantization.

Enhancing Wireless Networks for IoT with Large Vision Models: Foundations and Applications Vision Transformer-based Semantic Communications With Importance-Aware Quantization

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T10:06:14.008836Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 142cbeaa-3585-40d8-83f6-545db21f8b5e · outbound

This paper cites Swinjscc: Taming swin transformer for deep joint source-channel coding,.

Enhancing Wireless Networks for IoT with Large Vision Models: Foundations and Applications Swinjscc: Taming swin transformer for deep joint source-channel coding,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:06:14.299774Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ab59a602-cd8c-4c77-94e3-dcaa6b46df8a · outbound

This paper cites Dinov2-based uav visual self-localization in low-altitude urban environments,.

Enhancing Wireless Networks for IoT with Large Vision Models: Foundations and Applications Dinov2-based uav visual self-localization in low-altitude urban environments,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:06:14.275447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a28a7a59-e8d9-431d-aae4-be34428920ce · outbound

This paper cites Fully-decoupled ran for feedback-free multi-base station transmission in mimo-ofdm system,.

Enhancing Wireless Networks for IoT with Large Vision Models: Foundations and Applications Fully-decoupled ran for feedback-free multi-base station transmission in mimo-ofdm system,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:06:14.253703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e234d6fb-784f-4700-ad00-82881c6e38ba · outbound

This paper cites Viwi: A deep learning dataset framework for vision-aided wireless communications,.

Enhancing Wireless Networks for IoT with Large Vision Models: Foundations and Applications Viwi: A deep learning dataset framework for vision-aided wireless communications,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:06:14.229525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Pith citing papers

No inbound Pith citation observations are available.