Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Enhancing Automatic Modulation Recognition through Robust Global Feature Extraction
As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2401.01056.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-08T19:51:25.761624Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-03T02:27:34.586302Z
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 2de0401b-fa47-4680-9338-90c7807655e2 · inbound
AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities cites this paper.
AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities Enhancing Automatic Modulation Recognition through Robust Global Feature Extraction
Reference 8
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T19:51:25.761624Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6523e93f-361d-4147-86bb-75309789e1ea · inbound
Mixture-of-Experts Transformer for Automatic Modulation Recognition cites this paper.
Mixture-of-Experts Transformer for Automatic Modulation Recognition Enhancing Automatic Modulation Recognition through Robust Global Feature Extraction
Reference 49
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T02:27:34.587757Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 4fa19d4e-9af3-4d1b-93fc-cc0bd8e185c2 · inbound
An Uncertainty-Driven Hybrid Deep Learning Approach for Broad-Coverage RF Modulation Recognition cites this paper.
An Uncertainty-Driven Hybrid Deep Learning Approach for Broad-Coverage RF Modulation Recognition Enhancing Automatic Modulation Recognition through Robust Global Feature Extraction
Reference 21
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T00:15:37.665891Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.