Paper Citation Record · LEDGER

Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning

As of 10 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 2 inbound Pith citation observations for arXiv:2507.12910.

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

pith.paper-citation-record.v1
2507.12910 v1

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T16:45:56.582881Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T21:02:04.162159Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T20:53:58.379898Z

Reference resolution

44 of 44 outbound references displayed

verified exact1
verified fuzzy37
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 73d51c40-3562-4dc5-8154-d1cf604cb7f5 · outbound

This paper cites 6G: The Next Frontier: From Holographic Messaging to Artificial Intelligence Using Subterahertz and Visible Light Communication,.

Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning 6G: The Next Frontier: From Holographic Messaging to Artificial Intelligence Using Subterahertz and Visible Light Communication,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:46:02.991157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 53da8586-bc1c-4039-b182-f000c383d3f7 · outbound

This paper cites Survey on Multi-Access Edge Computing for Internet of Things Realization,.

Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning Survey on Multi-Access Edge Computing for Internet of Things Realization,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:46:02.845457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 79baab85-e4aa-48f5-858b-ac7a4cb6b18b · outbound

This paper cites Integration of D2D, Network Slicing, and MEC in 5G Cellular Networks: Survey and Challenges,.

Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning Integration of D2D, Network Slicing, and MEC in 5G Cellular Networks: Survey and Challenges,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:46:02.724990Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation dcd3b3d7-d941-42a5-aa47-715583730b23 · outbound

This paper cites Integrated Sensing and Communication for Low Alti- tude Economy: Opportunities and Challenges,.

Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning Integrated Sensing and Communication for Low Alti- tude Economy: Opportunities and Challenges,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:46:02.603835Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 35626717-e9c6-4bd1-a128-c2fb51b0bca9 · outbound

This paper cites Generative AI-enabled Wireless Communications for Robust Low-Altitude Economy Networking.

Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning Generative AI-enabled Wireless Communications for Robust Low-Altitude Economy Networking

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T16:45:52.320309Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 79631819-09da-4a05-8933-76d8891a02c3 · outbound

This paper cites Mobile Edge Computing via a UA V- Mounted Cloudlet: Optimization of Bit Allocation and Path Planning,.

Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning Mobile Edge Computing via a UA V- Mounted Cloudlet: Optimization of Bit Allocation and Path Planning,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:46:02.459202Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation be72fa46-74d4-4173-9408-3b6423c97369 · outbound

This paper cites Rate-Splitting Multiple Access: Fundamentals, Survey, and Future Research Trends,.

Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning Rate-Splitting Multiple Access: Fundamentals, Survey, and Future Research Trends,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:46:02.308182Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 32b686a1-94ca-4b32-8377-67bca41116ae · outbound

This paper cites Resource Allocation and User Pairing for Rate Splitting Multiple Access Based Wireless Networked Control Systems,.

Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning Resource Allocation and User Pairing for Rate Splitting Multiple Access Based Wireless Networked Control Systems,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:46:02.162791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 235cb2b2-3eac-4b52-bd49-618671eb43b9 · outbound

This paper cites Large-Scale Rate-Splitting Multiple Access in Uplink UA V Networks: Effective Secrecy Throughput Maximization Under Limited Feedback Channel,.

Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning Large-Scale Rate-Splitting Multiple Access in Uplink UA V Networks: Effective Secrecy Throughput Maximization Under Limited Feedback Channel,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:46:02.006794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 26789b9f-37a9-47ef-bf57-321468d6a7ef · outbound

This paper cites Multi- Objective Aerial Collaborative Secure Communication Optimization via Generative Diffusion Model-Enabled Deep Reinforcement Learning,.

Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning Multi- Objective Aerial Collaborative Secure Communication Optimization via Generative Diffusion Model-Enabled Deep Reinforcement Learning,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:46:01.865408Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation beebcbe2-0474-47a3-9093-4663c2900faf · outbound

This paper cites DRL-Driven Joint Task Offloading and Resource Alloca- tion for Energy-Efficient Content Delivery in Cloud-Edge Cooperation Networks,.

Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning DRL-Driven Joint Task Offloading and Resource Alloca- tion for Energy-Efficient Content Delivery in Cloud-Edge Cooperation Networks,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:46:01.713795Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9a1a7827-e4df-4b48-b65c-f07b18039931 · outbound

This paper cites ReaCritic: Large Reasoning Transformer-based DRL Critic-model Scaling For Heterogeneous Networks,.

Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning ReaCritic: Large Reasoning Transformer-based DRL Critic-model Scaling For Heterogeneous Networks,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T16:45:53.258128Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 17e1a6a9-0c36-4fb2-a5b3-cf923254d3c9 · outbound

This paper cites Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization,.

Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:46:01.590797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8f659c0d-7877-4224-8c8c-ebb8d0fc3432 · outbound

This paper cites Generative AI Enabled Matching for 6G Multiple Access.

Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning Generative AI Enabled Matching for 6G Multiple Access

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-08-06T16:45:56.783714Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 753ee779-bee7-40e7-8b93-b99b8db89c0b · outbound

This paper cites Generative Artificial Intelligence for Mobile Communications: A Diffusion Model Perspective,.

Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning Generative Artificial Intelligence for Mobile Communications: A Diffusion Model Perspective,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:46:01.466018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b2ddd169-584f-464a-b207-132d80428b9c · outbound

This paper cites DRESS: Diffusion Reasoning-based Reward Shaping Scheme For Intelligent Networks.

Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning DRESS: Diffusion Reasoning-based Reward Shaping Scheme For Intelligent Networks

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T16:45:53.799857Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 17b3f9dd-d755-4c0d-adb5-c6b1c5c758d4 · outbound

This paper cites Dynamic Offloading and Trajectory Control for UA V-Enabled Mobile Edge Computing System With Energy Harvesting Devices,.

Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning Dynamic Offloading and Trajectory Control for UA V-Enabled Mobile Edge Computing System With Energy Harvesting Devices,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:46:01.297647Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation acf81814-e4c6-4206-b6ec-9a26ce5ac04d · outbound

This paper cites Computation Efficiency Maximization and QoE-Provisioning in UA V-Enabled MEC Communication Systems,.

Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning Computation Efficiency Maximization and QoE-Provisioning in UA V-Enabled MEC Communication Systems,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:46:01.155535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b5f50789-a8c1-433c-969a-a3719ed4fe42 · outbound

This paper cites Resource Allocation and Trajectory Design for MISO UA V-Assisted MEC Networks,.

Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning Resource Allocation and Trajectory Design for MISO UA V-Assisted MEC Networks,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:46:00.984440Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 57d1269c-0413-4797-8d18-518d2ce885a0 · outbound

This paper cites Bi-Objective Ant Colony Optimization for Trajectory Planning and Task Offloading in UA V- Assisted MEC Systems,.

Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning Bi-Objective Ant Colony Optimization for Trajectory Planning and Task Offloading in UA V- Assisted MEC Systems,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:46:00.822368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f77359ce-d3c3-4c67-a616-dd0087f942d2 · outbound

This paper cites Optimal Trajectory and Resource Allocation for RSMA-UA V Assisted IoT Communications,.

Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning Optimal Trajectory and Resource Allocation for RSMA-UA V Assisted IoT Communications,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:46:00.686137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7b8d5b98-bb5a-4b1b-a945-f22702b29630 · outbound

This paper cites On the Physical Layer Security of the Cooperative Rate- Splitting-Aided Downlink in UA V Networks,.

Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning On the Physical Layer Security of the Cooperative Rate- Splitting-Aided Downlink in UA V Networks,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:46:00.536395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3488245f-9aec-4f0f-8114-675949685714 · outbound

This paper cites Rate Splitting on Mobile Edge Computing for UA V-Aided IoT Systems,.

Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning Rate Splitting on Mobile Edge Computing for UA V-Aided IoT Systems,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:46:00.345290Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 98c4fea1-4fa5-4c83-9bcd-ba1658ece14b · outbound

This paper cites PRU Group Allocation and Dynamic Rate-splitting Design for Power Minimization in IRS- assisted UA V MEC Systems With RSMA,.

Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning PRU Group Allocation and Dynamic Rate-splitting Design for Power Minimization in IRS- assisted UA V MEC Systems With RSMA,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:46:00.198836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a9785446-1b19-439d-a2c9-b5da97d0e263 · outbound

This paper cites an unresolved cited work.

Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning Unresolved cited work

Reference 25

Resolution: unresolved; raw_fallback, observed 2026-08-06T16:46:00.066632Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9e351313-b50a-4d5b-a30f-255439b3d426 · outbound

This paper cites Deep Reinforcement Learning Based Resource Allocation in Multi- UA V-Aided MEC Networks,.

Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning Deep Reinforcement Learning Based Resource Allocation in Multi- UA V-Aided MEC Networks,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:45:59.878741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2fe031e6-1f97-47a3-bcac-0882cc42ffa9 · outbound

This paper cites Optimizing Resource Allocation for Multi-Modal Semantic Communication in Mobile AIGC Networks: A Diffusion- Based Game Approach,.

Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning Optimizing Resource Allocation for Multi-Modal Semantic Communication in Mobile AIGC Networks: A Diffusion- Based Game Approach,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:45:59.731576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ff859b2f-dd4f-43e6-bbd6-184f411b9b5a · outbound

This paper cites Temporal Spectrum Cartography in Low-Altitude Economy Networks: A Generative AI Framework with Multi-Agent Learning.

Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning Temporal Spectrum Cartography in Low-Altitude Economy Networks: A Generative AI Framework with Multi-Agent Learning

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T16:45:55.094209Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bc443023-c0cc-4966-b735-b6a143f55e99 · outbound

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

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 2c9431fd-0e6a-4256-b5c5-72d1207c2d8e · outbound

This paper cites Effective Throughput Maximization for NOMA-Enabled URLLC Transmission in Industrial IoT Systems: A Generative AI-Based Approach,.

Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning Effective Throughput Maximization for NOMA-Enabled URLLC Transmission in Industrial IoT Systems: A Generative AI-Based Approach,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:45:59.557587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b1c7f76c-8046-469c-8c62-601ce31e4265 · outbound

This paper cites Enhanced Secure Beamforming for IRS-Assisted IoT Communication Using a Generative- Diffusion-Model-Enabled Optimization Approach,.

Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning Enhanced Secure Beamforming for IRS-Assisted IoT Communication Using a Generative- Diffusion-Model-Enabled Optimization Approach,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:45:59.339385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 95869b6a-7568-4f25-a8a2-2b18874f1595 · outbound

This paper cites A Unified Framework for Guiding Generative AI With Wireless Perception in Resource Constrained Mobile Edge Networks,.

Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning A Unified Framework for Guiding Generative AI With Wireless Perception in Resource Constrained Mobile Edge Networks,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:45:59.155012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation edcdc133-1cb8-47c0-b77c-a51be531859e · outbound

This paper cites Joint Trajectory-Task- Cache Optimization in UA V-Enabled Mobile Edge Networks for Cyber- Physical System,.

Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning Joint Trajectory-Task- Cache Optimization in UA V-Enabled Mobile Edge Networks for Cyber- Physical System,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:45:59.004587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f51f6b92-7ff2-4ce2-b0be-ca2e43cc61a4 · outbound

This paper cites Optimal LAP Altitude for Maximum Coverage,.

Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning Optimal LAP Altitude for Maximum Coverage,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:45:58.836646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b62457c9-8122-407b-b5d8-df2e9e03ec5e · outbound

This paper cites Energy-Efficient Resource Allocation in UA V Based MEC System for IoT Devices,.

Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning Energy-Efficient Resource Allocation in UA V Based MEC System for IoT Devices,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:45:58.670610Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7cf56f6f-06b3-4018-a530-4486a0fd9275 · outbound

This paper cites Sum- Rate Maximization of Uplink Rate Splitting Multiple Access (RSMA) Communication,.

Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning Sum- Rate Maximization of Uplink Rate Splitting Multiple Access (RSMA) Communication,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:45:58.459855Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1f30cbd2-7652-4649-b358-79a779584326 · outbound

This paper cites Power Efficient IRS-Assisted NOMA,.

Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning Power Efficient IRS-Assisted NOMA,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:45:58.268911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 60ba1df3-5cdd-441c-9d75-37644b120854 · outbound

This paper cites Sum Rate Maximization for IRS-Assisted Uplink NOMA,.

Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning Sum Rate Maximization for IRS-Assisted Uplink NOMA,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:45:58.051835Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 168eeaae-01f7-4a21-80d9-469fe3f29ae8 · outbound

This paper cites Optimal and Sub-Optimal Uplink NOMA: Joint User Grouping, Decoding Order, and Power Control,.

Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning Optimal and Sub-Optimal Uplink NOMA: Joint User Grouping, Decoding Order, and Power Control,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:45:57.833587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 53638781-a734-478e-b914-3317389e8831 · outbound

This paper cites Deep Reinforcement Learning for Minimizing Age-of-Information in UA V- Assisted Networks,.

Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning Deep Reinforcement Learning for Minimizing Age-of-Information in UA V- Assisted Networks,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:45:57.681586Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c7cf7cff-f047-4bde-87f5-5968d77106f2 · outbound

This paper cites Improved Denoising Diffusion Prob- abilistic Models,.

Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning Improved Denoising Diffusion Prob- abilistic Models,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:45:57.492674Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e873a481-1b0f-4078-b6ec-bb4f23baabef · outbound

This paper cites Denoising Diffusion Probabilistic Models, author=Ho, Jonathan and Jain, Ajay and Abbeel, Pieter,.

Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning Denoising Diffusion Probabilistic Models, author=Ho, Jonathan and Jain, Ajay and Abbeel, Pieter,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:45:57.308745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 56fe6382-c844-40ff-94be-87b6ad786575 · outbound

This paper cites Diffusion-Based Reinforcement Learning for Edge-Enabled AI-Generated Content Services,.

Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning Diffusion-Based Reinforcement Learning for Edge-Enabled AI-Generated Content Services,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:45:57.160689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c4a14f72-01dd-4d90-9419-17ef4bc45e43 · outbound

This paper cites Joint Deployment and Resource Allocation for Multi-AeBS Networks: A Two-Timescale Optimization Framework Using MADRL,.

Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning Joint Deployment and Resource Allocation for Multi-AeBS Networks: A Two-Timescale Optimization Framework Using MADRL,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:45:56.978205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

Observation 42b7274b-1266-4892-bfe4-0f3599d64cad · inbound

Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges cites this paper.

Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-05T21:02:04.162159Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7ad879fd-a3f8-4982-8454-2c75447d43a6 · 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 Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning

Reference 74

Resolution: verified exact; arxiv_id, observed 2026-06-29T20:53:58.381394Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.