Paper Citation Record · LEDGER

Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models

As of 9 August 2026, this Paper Citation Record lists 41 of 41 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.

pith.paper-citation-record.v1
2502.07211 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T13:32:44.415680Z

measured 44 of 44 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 2026-08-06T16:45:55.178372Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T20:53:58.327292Z

Reference resolution

41 of 41 outbound references displayed

verified exact2
verified fuzzy33
unresolved6
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 74646eed-cde1-47e9-b7e2-9798b0baf84b · outbound

This paper cites Optimal power allocation for rate splitting communications with deep reinforcement learning,.

Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models Optimal power allocation for rate splitting communications with deep reinforcement learning,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:32:45.447685Z

Source-reported events for the cited work

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Observation 2ca00c12-41e9-4e82-9d59-9319ee86724f · outbound

This paper cites UA V- assisted MEC system with mobile ground terminals: DRL-based joint terminal scheduling and UA V 3D trajectory design,.

Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models UA V- assisted MEC system with mobile ground terminals: DRL-based joint terminal scheduling and UA V 3D trajectory design,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:32:45.433058Z

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.

Observation b6393dda-e007-48a4-bc41-f80b13d96b50 · outbound

This paper cites Full-duplex communication for ISAC: Joint beamforming and power optimization,.

Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models Full-duplex communication for ISAC: Joint beamforming and power optimization,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:32:45.418509Z

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.

Observation edfb85df-61fa-4c59-9bc8-84d51679745e · outbound

This paper cites Full-duplex cooperative NOMA with signal space diversity: Minimizing SIC operations,.

Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models Full-duplex cooperative NOMA with signal space diversity: Minimizing SIC operations,

Reference 4

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-08T13:32:44.912969Z

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.

Observation edeee3b0-ed69-456a-b2b7-efcc33616d23 · outbound

This paper cites A DRL approach for RIS-assisted full-duplex UL and DL transmission: Beamforming, phase shift and power optimization,.

Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models A DRL approach for RIS-assisted full-duplex UL and DL transmission: Beamforming, phase shift and power optimization,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:32:45.402687Z

Source-reported events for the cited work

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Observation ff49da1b-7a0c-47b5-b12d-4eaef7edc7d2 · outbound

This paper cites Conservative q-learning for offline reinforcement learning,.

Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models Conservative q-learning for offline reinforcement learning,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:32:45.387224Z

Source-reported events for the cited work

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Observation 29a622bc-f03f-4efc-b08b-4c5af3fbf55e · outbound

This paper cites An optimistic perspec- tive on offline reinforcement learning,.

Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models An optimistic perspec- tive on offline reinforcement learning,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:32:45.371635Z

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.

Observation 0a12c736-36cc-4d4d-ad12-64e3f1ea33e2 · outbound

This paper cites Deep-reinforcement-learning-based sustainable energy distribution for wireless communication,.

Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models Deep-reinforcement-learning-based sustainable energy distribution for wireless communication,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:32:45.356004Z

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.

Observation ba51e293-7f8f-4b58-9695-c2d09f9f177f · outbound

This paper cites VWP: An efficient DRL- based autonomous driving model,.

Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models VWP: An efficient DRL- based autonomous driving model,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:32:45.340597Z

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.

Observation b4ef4317-90e8-433c-925d-42e442b6d67d · outbound

This paper cites Offline DRL for price-based demand response: Learning from suboptimal data and beyond,.

Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models Offline DRL for price-based demand response: Learning from suboptimal data and beyond,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:32:45.325533Z

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.

Observation 7ce8d549-ce19-4e40-99f5-38aa4b09ca49 · outbound

This paper cites Provably efficient causal reinforcement learning with confounded observational data,.

Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models Provably efficient causal reinforcement learning with confounded observational data,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:32:45.310189Z

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.

Observation 9cb64647-991e-42b3-901a-b211a0f03c92 · outbound

This paper cites Vrl3: A data-driven framework for visual deep reinforcement learning,.

Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models Vrl3: A data-driven framework for visual deep reinforcement learning,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:32:45.293308Z

Source-reported events for the cited work

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Observation 8c5bc9fa-72cf-4cc3-a14d-33e15be4e9ef · outbound

This paper cites Optimizing deep reinforcement learning in data-scarce domains: A cross-domain evaluation of double DQN and dueling DQN,.

Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models Optimizing deep reinforcement learning in data-scarce domains: A cross-domain evaluation of double DQN and dueling DQN,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:32:45.275975Z

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.

Observation 021a5cee-f1bc-46e5-b33c-bb04c585e6b3 · outbound

This paper cites Structure-enhanced DRL for optimal transmission scheduling,.

Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models Structure-enhanced DRL for optimal transmission scheduling,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:32:45.260537Z

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.

Observation 80e47365-ef4f-4799-a910-e53086418e9a · outbound

This paper cites Understanding Learned Reward Functions.

Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models Understanding Learned Reward Functions

Reference 15

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Source-reported events for the cited work

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Observation 9055eacb-24c4-416a-ab61-5e475b74d509 · outbound

This paper cites Movement and communication co-design in multi-UA V enabled wireless systems via DRL,.

Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models Movement and communication co-design in multi-UA V enabled wireless systems via DRL,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:32:45.244322Z

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.

Observation b0683c4d-3b81-48ce-8e49-7d50b80dcf31 · outbound

This paper cites Enhancing deep reinforcement learning: A tutorial on generative diffusion models in network optimization,.

Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models Enhancing deep reinforcement learning: A tutorial on generative diffusion models in network optimization,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:32:45.228014Z

Source-reported events for the cited work

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Observation 5f222812-38d4-438a-8e6b-d2c0b40b05fd · outbound

This paper cites Deep unsupervised learning using nonequilibrium thermodynamics,.

Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models Deep unsupervised learning using nonequilibrium thermodynamics,

Reference 18

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Source-reported events for the cited work

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Observation 4c160ca8-fe34-4c56-9461-b6aa38c1eedb · outbound

This paper cites Deep reinforcement learning-based resource allocation for D2D communications in hetero- geneous cellular networks,.

Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models Deep reinforcement learning-based resource allocation for D2D communications in hetero- geneous cellular networks,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:32:45.202520Z

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.

Observation 641bd11e-82e1-4ee6-9009-00a79ba741a4 · outbound

This paper cites Deep reinforcement learning-based resource allocation in cooperative UA V-assisted wireless networks,.

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:32:45.187717Z

Source-reported events for the cited work

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Observation a0f4616d-ef6c-435e-8717-a457b9355ba6 · outbound

This paper cites Hybrid hierarchical DRL enabled resource allocation for secure transmission in multi-IRS-assisted sensing-enhanced spectrum sharing networks,.

Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models Hybrid hierarchical DRL enabled resource allocation for secure transmission in multi-IRS-assisted sensing-enhanced spectrum sharing networks,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:32:45.172530Z

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.

Observation 12847825-9f19-4b8b-8d47-48a6427e3b15 · outbound

This paper cites DRL-based energy-efficient resource allocation frameworks for uplink NOMA sys- tems,.

Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models DRL-based energy-efficient resource allocation frameworks for uplink NOMA sys- tems,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:32:45.157598Z

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.

Observation 08ece4e3-d3f4-449e-b592-cf753fd1173b · outbound

This paper cites DRL-driven dynamic resource allocation for task-oriented semantic communication,.

Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models DRL-driven dynamic resource allocation for task-oriented semantic communication,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:32:45.141774Z

Source-reported events for the cited work

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Observation 83971489-a57c-4a33-b91b-d6bde04f88b3 · outbound

This paper cites Multi-agent DRL approach for energy-efficient resource allocation in URLLC-enabled grant-free NOMA systems,.

Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models Multi-agent DRL approach for energy-efficient resource allocation in URLLC-enabled grant-free NOMA systems,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:32:45.125868Z

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.

Observation 022b3600-0c9b-4ffd-8a79-ad4b1a398370 · outbound

This paper cites Planning with Diffusion for Flexible Behavior Synthesis.

Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models Planning with Diffusion for Flexible Behavior Synthesis

Reference 25

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Observation adb51c2c-2cf3-4aae-9936-8c96ed78b31e · outbound

This paper cites Hybrid-Generative Diffusion Models for Attack-Oriented Twin Migration in Vehicular Metaverses.

Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models Hybrid-Generative Diffusion Models for Attack-Oriented Twin Migration in Vehicular Metaverses

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-08T13:32:44.344638Z

Source-reported events for the cited work

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Observation 2f32e4c8-2c85-4188-a4ef-f314ff3dd5cb · outbound

This paper cites Diffusion-based reinforcement learning for edge-enabled AI-generated content services,.

Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models Diffusion-based reinforcement learning for edge-enabled AI-generated content services,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:32:45.110225Z

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.

Observation 10fdecd0-c849-4d6e-909c-61f9b208a2f9 · outbound

This paper cites Diffusiongpt: LLM-driven text-to-image generation system,.

Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models Diffusiongpt: LLM-driven text-to-image generation system,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-08T13:32:44.354309Z

Source-reported events for the cited work

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Observation f9032555-abd6-4ef1-adf4-f6d078c1d8c3 · outbound

This paper cites Multi- agent DRL for task offloading and resource allocation in multi-UA V enabled iot edge network,.

Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models Multi- agent DRL for task offloading and resource allocation in multi-UA V enabled iot edge network,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:32:45.094793Z

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.

Observation e4148416-f2f7-4e42-a20a-7da77bfb80f0 · outbound

This paper cites DRL-based partial offloading for maximizing sum computation rate of wireless pow- ered mobile edge computing network,.

Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models DRL-based partial offloading for maximizing sum computation rate of wireless pow- ered mobile edge computing network,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:32:45.078736Z

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.

Observation bf062ded-15cf-475b-8a65-2388a22feefd · outbound

This paper cites Delay-oriented scheduling in 5G downlink wireless networks based on reinforcement learning with partial observations,.

Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models Delay-oriented scheduling in 5G downlink wireless networks based on reinforcement learning with partial observations,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:32:45.062752Z

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.

Observation 0016ca04-4224-4d21-84df-412da3dddd96 · outbound

This paper cites D- RAN: A DRL-based demand-driven elastic user-centric RAN optimiza- tion for 6G & beyond,.

Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models D- RAN: A DRL-based demand-driven elastic user-centric RAN optimiza- tion for 6G & beyond,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:32:45.045462Z

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.

Observation b86d880a-bd7f-44e2-9065-6ca5fe46a1a0 · outbound

This paper cites QoS- DRAMA: Quality of service aware drl-based adaptive mid-level resource allocation scheme,.

Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models QoS- DRAMA: Quality of service aware drl-based adaptive mid-level resource allocation scheme,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:32:45.027937Z

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.

Observation e8da0d52-1617-46eb-8db3-accfaa8bf1b1 · outbound

This paper cites Self-organizing mmwave MIMO cell-free networks with hybrid beamforming: A hierarchical DRL-based design,.

Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models Self-organizing mmwave MIMO cell-free networks with hybrid beamforming: A hierarchical DRL-based design,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:32:45.011641Z

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.

Observation 5330fc04-651e-4256-b0a4-496c5b933357 · outbound

This paper cites Reinforcement learning based down- link OFDMA scheduling for time-sensitive wifi networks,.

Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models Reinforcement learning based down- link OFDMA scheduling for time-sensitive wifi networks,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:32:44.993231Z

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.

Observation c5d04a4d-f253-4b99-bd7f-67a2ee41b567 · outbound

This paper cites DRL-based dynamic resource allocation for multi-beam satellite systems,.

Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models DRL-based dynamic resource allocation for multi-beam satellite systems,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:32:44.977495Z

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.

Observation 65cfa1f1-9239-49f0-85d5-4e359ba3f9f9 · outbound

This paper cites Denoising Diffusion Implicit Models.

Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models Denoising Diffusion Implicit Models

Reference 37

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Observation a1e9ff02-0f66-49c2-88ea-3b2bb782b244 · outbound

This paper cites Attention- based QoE-aware digital twin empowered edge computing for immersive virtual reality,.

Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models Attention- based QoE-aware digital twin empowered edge computing for immersive virtual reality,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:32:44.961248Z

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.

Observation 394e359f-059e-4b97-8f40-77c5b4bb105c · outbound

This paper cites Intelligent resource allocation for edge-cloud collaborative networks: A hybrid DDPG-D3QN approach,.

Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models Intelligent resource allocation for edge-cloud collaborative networks: A hybrid DDPG-D3QN approach,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:32:44.945004Z

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.

Observation 5418d0b8-4165-4ed0-a57d-9c5c256c6d00 · outbound

This paper cites Utility-oriented optimization for video streaming in UA V-aided MEC network: A DRL approach,.

Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models Utility-oriented optimization for video streaming in UA V-aided MEC network: A DRL approach,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:32:44.929687Z

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.

Observation 91646c55-1093-4661-999f-a4f9bf88bf7a · outbound

This paper cites Federated Prompt-based Decision Transformer for Customized VR Services in Mobile Edge Computing System.

Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models Federated Prompt-based Decision Transformer for Customized VR Services in Mobile Edge Computing System

Reference 41

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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.

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

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Source-reported events for the cited work

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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 2026-08-09T06:31:02.800959+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

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.