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Paper Citation Record · LEDGER

Leveraging Self-Supervised Learning for MIMO-OFDM Channel Representation and Generation

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2407.07702.

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

pith.paper-citation-record.v1
2407.07702 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:30:29.964420Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T05:33:56.339239Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 26c0607e-73d7-464e-8a5a-ab30ef4f6cf5 · inbound

CSI2Vec: Towards a Universal CSI Feature Representation for Positioning and Channel Charting cites this paper.

CSI2Vec: Towards a Universal CSI Feature Representation for Positioning and Channel Charting Leveraging Self-Supervised Learning for MIMO-OFDM Channel Representation and Generation

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-07T10:30:29.964420Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:30:29.964420Z digest=sha256:38183640e89f355f8b6483b76254e3110a8f9c70f368b6b8ce580f1dd77557cf

Observation 6d74f95b-3dee-4836-a8ce-0120fa96d538 · inbound

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

IQFM A Wireless Foundational Model for I/Q Streams in AI-Native 6G Leveraging Self-Supervised Learning for MIMO-OFDM Channel Representation and Generation

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-07T05:56:03.137828Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:56:03.137828Z digest=sha256:0b808d05aa90a098d92f939879cf34018c6ab8d003a6fa254ccd1d3d3df23ad2

Observation 27b56a11-66d3-408d-8ce9-5f234c577165 · inbound

Foundation Model Empowered Synesthesia of Machines (SoM): AI-native Intelligent Multi-Modal Sensing-Communication Integration cites this paper.

Foundation Model Empowered Synesthesia of Machines (SoM): AI-native Intelligent Multi-Modal Sensing-Communication Integration Leveraging Self-Supervised Learning for MIMO-OFDM Channel Representation and Generation

Reference 101

Resolution
verified exact
local_arxiv, observed 2026-08-07T05:33:56.344725Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T05:33:55.798851Z digest=sha256:96d5a4e3801b37be7799f45c89fcc38ba80bd20b2b084fa2385c3f3472e95e46