Paper Citation Record · LEDGER

Radio-FM: A Foundation Model for Radio Signal Representation Learning and Its Applications

As of 14 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2608.05793.

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

pith.paper-citation-record.v1
2608.05793 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T23:29:04.050542Z

measured 43 of 43 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

43 of 43 outbound references displayed

verified exact1
verified fuzzy24
unresolved18
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5bb132ac-fe50-4081-abc5-0a169463cace · outbound

This paper cites Building 6G Radio Foundation Models with Transformer Architectures.

Radio-FM: A Foundation Model for Radio Signal Representation Learning and Its Applications Building 6G Radio Foundation Models with Transformer Architectures

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T23:29:03.850180Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 14e29186-cff2-437b-8e73-8ef53da4a1fa · outbound

This paper cites 6G WavesFM: A Foundation Model for Sensing, Communication, and Localization.

Radio-FM: A Foundation Model for Radio Signal Representation Learning and Its Applications 6G WavesFM: A Foundation Model for Sensing, Communication, and Localization

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T23:29:03.856048Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7acd08f7-09f2-42e0-bce9-5e19a3afe0b7 · outbound

This paper cites Towards channel foundation models (CFMs): Motivations, methodologies and opportunities.

Radio-FM: A Foundation Model for Radio Signal Representation Learning and Its Applications Towards channel foundation models (CFMs): Motivations, methodologies and opportunities

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T23:29:03.861286Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ae03e123-7b7a-410c-ae0d-a19afb8b39db · outbound

This paper cites Wifo: Wireless foundation model for channel prediction,.

Radio-FM: A Foundation Model for Radio Signal Representation Learning and Its Applications Wifo: Wireless foundation model for channel prediction,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T23:29:03.866429Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f20868a3-825b-4576-9aaf-205bf9dad3eb · outbound

This paper cites Tiny federated wireless foundation models for resource constrained devices,.

Radio-FM: A Foundation Model for Radio Signal Representation Learning and Its Applications Tiny federated wireless foundation models for resource constrained devices,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T23:29:03.871662Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e0e2d74-a93f-4390-99de-7fe926add973 · outbound

This paper cites Scale what counts, mask what matters: Evaluating foundation models for zero-shot cross- domain wi-fi sensing,.

Radio-FM: A Foundation Model for Radio Signal Representation Learning and Its Applications Scale what counts, mask what matters: Evaluating foundation models for zero-shot cross- domain wi-fi sensing,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T23:29:03.876837Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6cadefc9-641d-4461-944d-b03adbc55d26 · outbound

This paper cites Multimodal wireless foundation models,.

Radio-FM: A Foundation Model for Radio Signal Representation Learning and Its Applications Multimodal wireless foundation models,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T23:29:03.882283Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5b4d32a6-5f1c-44cc-922f-32915368c1ee · outbound

This paper cites Large Wireless Localization Model (LWLM): A Foundation Model for Positioning in 6G Networks.

Radio-FM: A Foundation Model for Radio Signal Representation Learning and Its Applications Large Wireless Localization Model (LWLM): A Foundation Model for Positioning in 6G Networks

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T23:29:03.886803Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 81e01274-3ce1-4da3-beb0-3ecacb118948 · outbound

This paper cites Rf-diffusion: Radio signal generation via time-frequency diffusion,.

Radio-FM: A Foundation Model for Radio Signal Representation Learning and Its Applications Rf-diffusion: Radio signal generation via time-frequency diffusion,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:29:04.995828Z

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 003a417f-ad6f-47d2-ae06-1d26ecdd67e3 · outbound

This paper cites WirelessGPT: A Generative Pre-trained Multi-task Learning Framework for Wireless Communication.

Radio-FM: A Foundation Model for Radio Signal Representation Learning and Its Applications WirelessGPT: A Generative Pre-trained Multi-task Learning Framework for Wireless Communication

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T23:29:03.896033Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4406adf5-8aad-455c-9241-a8f639cac6c2 · outbound

This paper cites RIS-MAE: A Self-Supervised Modulation Classification Method Based on Raw IQ Signals and Masked Autoencoder.

Radio-FM: A Foundation Model for Radio Signal Representation Learning and Its Applications RIS-MAE: A Self-Supervised Modulation Classification Method Based on Raw IQ Signals and Masked Autoencoder

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-08-07T23:29:04.143372Z

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 d8e83287-c6c8-4dec-93bf-7ee4b7371a85 · outbound

This paper cites Spectrumfm: A foundation model for intelligent spectrum management,.

Radio-FM: A Foundation Model for Radio Signal Representation Learning and Its Applications Spectrumfm: A foundation model for intelligent spectrum management,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:29:04.980245Z

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 5da6ccf4-4fd6-469b-8b10-892250bf2269 · outbound

This paper cites EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding.

Radio-FM: A Foundation Model for Radio Signal Representation Learning and Its Applications EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T23:29:03.910400Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2f2bc4fc-8cb8-4655-9742-68cd7ee8a99c · outbound

This paper cites Large- scale real-world radio signal recognition with deep learning,.

Radio-FM: A Foundation Model for Radio Signal Representation Learning and Its Applications Large- scale real-world radio signal recognition with deep learning,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:29:04.964351Z

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 c5c1c950-9c8b-42f5-9f56-059b8da0887d · outbound

This paper cites Convolutional radio modula- tion recognition networks,.

Radio-FM: A Foundation Model for Radio Signal Representation Learning and Its Applications Convolutional radio modula- tion recognition networks,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:29:04.947606Z

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 1180b924-2ab2-4ab3-89c1-0a1c36af0b4e · outbound

This paper cites Robust and fast automatic modulation classification with cnn under multipath fading channels,.

Radio-FM: A Foundation Model for Radio Signal Representation Learning and Its Applications Robust and fast automatic modulation classification with cnn under multipath fading channels,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:29:04.922525Z

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 8d9309c9-168b-4a26-8c57-6322b3aaf5de · outbound

This paper cites Signet: A novel deep learning framework for radio signal classification,.

Radio-FM: A Foundation Model for Radio Signal Representation Learning and Its Applications Signet: A novel deep learning framework for radio signal classification,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:29:04.905988Z

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 c4ec6ab4-641a-4601-98a7-aff25efe333d · outbound

This paper cites Contour stella image and deep learning for signal recognition in the physical layer,.

Radio-FM: A Foundation Model for Radio Signal Representation Learning and Its Applications Contour stella image and deep learning for signal recognition in the physical layer,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:29:04.889193Z

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 92cf1cca-67fc-4f77-8e59-06ada3d450ea · outbound

This paper cites Complex-valued networks for automatic modulation classification,.

Radio-FM: A Foundation Model for Radio Signal Representation Learning and Its Applications Complex-valued networks for automatic modulation classification,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:29:04.873159Z

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 3a793588-6e4f-4d10-9d55-a3c667b4b56f · outbound

This paper cites Semi-supervised learning with generative adversarial networks on digital signal modulation classifica- tion.,.

Radio-FM: A Foundation Model for Radio Signal Representation Learning and Its Applications Semi-supervised learning with generative adversarial networks on digital signal modulation classifica- tion.,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:29:04.857511Z

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 c6513392-67ff-4964-8e4d-a37fa664cb21 · outbound

This paper cites Avgnet: Adaptive visibility graph neural network and its application in modulation classification,.

Radio-FM: A Foundation Model for Radio Signal Representation Learning and Its Applications Avgnet: Adaptive visibility graph neural network and its application in modulation classification,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T23:29:03.948073Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 674d3e8b-333c-493b-92ba-0e609c65712c · outbound

This paper cites Dtsg-net: Dynamic time series graph neural network and it’s application in modulation recognition,.

Radio-FM: A Foundation Model for Radio Signal Representation Learning and Its Applications Dtsg-net: Dynamic time series graph neural network and it’s application in modulation recognition,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:29:04.830664Z

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 cd9529be-0436-4dc1-b048-410cb6e8419e · outbound

This paper cites Lstm framework for classification of radar and communications signals,.

Radio-FM: A Foundation Model for Radio Signal Representation Learning and Its Applications Lstm framework for classification of radar and communications signals,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:29:04.814993Z

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 aa60d936-e2c3-43d4-ab04-8d5bdb660b46 · outbound

This paper cites Multi- task learning for radar signal characterisation,.

Radio-FM: A Foundation Model for Radio Signal Representation Learning and Its Applications Multi- task learning for radar signal characterisation,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:29:04.798867Z

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 3f18f6fc-6da6-4b31-bed1-3b142f1b4e00 · outbound

This paper cites Wisig: A large-scale wifi signal dataset for receiver and channel agnostic rf fingerprinting,.

Radio-FM: A Foundation Model for Radio Signal Representation Learning and Its Applications Wisig: A large-scale wifi signal dataset for receiver and channel agnostic rf fingerprinting,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T23:29:03.966645Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 875feea4-0a5e-4954-95b3-490d92e3970e · outbound

This paper cites Trust in 5g open rans through machine learning: Rf fingerprinting on the powder pawr platform,.

Radio-FM: A Foundation Model for Radio Signal Representation Learning and Its Applications Trust in 5g open rans through machine learning: Rf fingerprinting on the powder pawr platform,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:29:04.681110Z

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 8a5ef0c9-9d8a-4150-9383-9184395e6441 · outbound

This paper cites Radio frequency fingerprint identification towards statistical and deep learning features: Review, recent results and future directions,.

Radio-FM: A Foundation Model for Radio Signal Representation Learning and Its Applications Radio frequency fingerprint identification towards statistical and deep learning features: Review, recent results and future directions,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:29:04.665007Z

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 0d18b96c-c47d-4dd0-bcd4-888bebce7ebd · outbound

This paper cites Tfmix: A robust time-frequency mixing approach for domain generalization in specific emitter identification,.

Radio-FM: A Foundation Model for Radio Signal Representation Learning and Its Applications Tfmix: A robust time-frequency mixing approach for domain generalization in specific emitter identification,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:29:04.648927Z

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 cd30b08e-b392-44f6-be38-d1c446aa6828 · outbound

This paper cites Towards low-complexity wireless technology classification across multiple environments,.

Radio-FM: A Foundation Model for Radio Signal Representation Learning and Its Applications Towards low-complexity wireless technology classification across multiple environments,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:29:04.632786Z

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 e61387f0-3878-4815-a8f5-605ce72781ea · outbound

This paper cites Multi-band sub-ghz technology recognition on nvidia’s jetson nano,.

Radio-FM: A Foundation Model for Radio Signal Representation Learning and Its Applications Multi-band sub-ghz technology recognition on nvidia’s jetson nano,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:29:04.616022Z

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 01fd11e9-691e-4de7-a174-9dceb867a7ba · outbound

This paper cites Wireless interference iden- tification with convolutional neural networks,.

Radio-FM: A Foundation Model for Radio Signal Representation Learning and Its Applications Wireless interference iden- tification with convolutional neural networks,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:29:04.600631Z

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 835231ff-a09f-4d6a-8bf7-3d371d00776c · outbound

This paper cites Deep learning for interference identification: Band, training snr, and sample selection,.

Radio-FM: A Foundation Model for Radio Signal Representation Learning and Its Applications Deep learning for interference identification: Band, training snr, and sample selection,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:29:04.585558Z

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 16102a72-3792-4c3d-8230-4d02c2a76822 · outbound

This paper cites IQFM A Wireless Foundational Model for I/Q Streams in AI-Native 6G.

Radio-FM: A Foundation Model for Radio Signal Representation Learning and Its Applications IQFM A Wireless Foundational Model for I/Q Streams in AI-Native 6G

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-07T23:29:04.004885Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4293d2a5-b2fe-4d43-a216-04d8385ae6a8 · outbound

This paper cites A foundation model for wireless technology recognition and localiza- tion tasks,.

Radio-FM: A Foundation Model for Radio Signal Representation Learning and Its Applications A foundation model for wireless technology recognition and localiza- tion tasks,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:29:04.570259Z

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 6e1ffada-b9f6-4584-b9a5-219ab81c6472 · outbound

This paper cites Skyllm: Enabling trustworthy uav rf surveillance with foundation models for open-world signal recognition,.

Radio-FM: A Foundation Model for Radio Signal Representation Learning and Its Applications Skyllm: Enabling trustworthy uav rf surveillance with foundation models for open-world signal recognition,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:29:04.553230Z

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 4c534b17-80f9-406f-ad92-75695ac9c367 · outbound

This paper cites Roformer: En- hanced transformer with rotary position embedding,.

Radio-FM: A Foundation Model for Radio Signal Representation Learning and Its Applications Roformer: En- hanced transformer with rotary position embedding,

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T23:29:04.018393Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a969d7c3-6706-41d0-b001-17a162aeecbf · outbound

This paper cites Go- ing deeper with image transformers,.

Radio-FM: A Foundation Model for Radio Signal Representation Learning and Its Applications Go- ing deeper with image transformers,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:29:04.524859Z

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 acea6177-5f88-408b-b40e-3e9d6b129dad · outbound

This paper cites Deep networks with stochastic depth,.

Radio-FM: A Foundation Model for Radio Signal Representation Learning and Its Applications Deep networks with stochastic depth,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:29:04.509184Z

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 c6a3a1f3-a81d-49d3-9dc1-7ec0a6c681a8 · outbound

This paper cites Toward next-generation signal intelligence: A hybrid knowledge and data-driven deep learning framework for radio signal classification,.

Radio-FM: A Foundation Model for Radio Signal Representation Learning and Its Applications Toward next-generation signal intelligence: A hybrid knowledge and data-driven deep learning framework for radio signal classification,

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T23:29:04.032125Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3cdff6a4-5c97-4c1c-af21-ef4a91ed7ce9 · outbound

This paper cites Over-the-air deep learning based radio signal classification,.

Radio-FM: A Foundation Model for Radio Signal Representation Learning and Its Applications Over-the-air deep learning based radio signal classification,

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T23:29:04.036560Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 61b67885-9ea1-46d1-aefb-71fe91957423 · outbound

This paper cites Dataset for modulation classification and signal type classification for multi-task and single task learning,.

Radio-FM: A Foundation Model for Radio Signal Representation Learning and Its Applications Dataset for modulation classification and signal type classification for multi-task and single task learning,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:29:04.473415Z

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 558946c5-66d1-403f-8de2-de1ecb20f0cc · outbound

This paper cites Large Scale Radio Frequency Signal Classification.

Radio-FM: A Foundation Model for Radio Signal Representation Learning and Its Applications Large Scale Radio Frequency Signal Classification

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T23:29:04.045536Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1aca6fe5-5750-4248-92e6-2e0b07af5487 · outbound

This paper cites Rml22: Realistic dataset generation for wireless modulation classification,.

Radio-FM: A Foundation Model for Radio Signal Representation Learning and Its Applications Rml22: Realistic dataset generation for wireless modulation classification,

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T23:29:04.050542Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.