Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2502.07211.
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 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T16:45:55.178372Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-06-29T20:53:58.327292Z
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 bc443023-c0cc-4966-b735-b6a143f55e99 · inbound
Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning cites this paper.
Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models
Reference 29
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T16:45:55.178372Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 29967abd-53e5-4e22-86f0-ea0480e7e350 · inbound
From Denoising to Decision Making: A Survey on Diffusion Model-Enabled Deep Reinforcement Learning for Wireless Networks cites this paper.
From Denoising to Decision Making: A Survey on Diffusion Model-Enabled Deep Reinforcement Learning for Wireless Networks Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models
Reference 22
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T20:53:58.328893Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 5ebef4e4-9ea0-422d-9616-7e7856fbb817 · inbound
Sustainable Air-Ground Integrated Coverage Networks: ISCC Architecture, Technologies, and Testbed cites this paper.
Sustainable Air-Ground Integrated Coverage Networks: ISCC Architecture, Technologies, and Testbed Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models
Reference 87
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T19:56:12.280337Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.