Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach | Paper Citation Record

Observation d5ba96b6-405e-4ee5-83a4-5926667fd632 · outbound

This paper cites Energy-efﬁ cient UA V control for effective and fair communication coverage: A deep reinforcement learning approach,.

Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach Energy-efﬁ cient UA V control for effective and fair communication coverage: A deep reinforcement learning approach,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:39:28.365503Z

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Observation c8f6cf6a-5dd7-4d63-8a67-697e4897050e · outbound

This paper cites UA V communications based on non-orthogonal multiple access,.

Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach UA V communications based on non-orthogonal multiple access,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:39:28.357290Z

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Observation 4172e8ef-f74a-4fc1-87c8-b0f4f18526eb · outbound

This paper cites A survey of channel modeling for UA V communications,.

Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach A survey of channel modeling for UA V communications,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:39:28.348043Z

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Observation 371f8335-39bc-4d58-afe0-2301fd38258c · outbound

This paper cites Optimal 3D-trajectory design and resource allocation for solar-po wered UA V communication systems,.

Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach Optimal 3D-trajectory design and resource allocation for solar-po wered UA V communication systems,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:39:28.336849Z

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Observation b80dd150-0e47-4975-b666-b4eeaced34e7 · outbound

This paper cites Energy efﬁcient 3 - D UA V control for persistent communication service and fair ness: A deep reinforcement learning approach,.

Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach Energy efﬁcient 3 - D UA V control for persistent communication service and fair ness: A deep reinforcement learning approach,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:39:28.326871Z

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Observation c5530a58-c4ec-4ca6-a397-e4a5f6a8753d · outbound

This paper cites Wireles s communication using unmanned aerial vehicles (UA Vs): Opti mal transport theory for hover time optimization,.

Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach Wireles s communication using unmanned aerial vehicles (UA Vs): Opti mal transport theory for hover time optimization,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:39:28.317423Z

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Observation 98cd6dbe-4546-452c-ad88-f49364097b83 · outbound

This paper cites UA V communications for 5G and beyond: Recent advances and future trends,.

Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach UA V communications for 5G and beyond: Recent advances and future trends,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:39:28.307545Z

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Observation 0963c19f-5623-4ffa-b13e-175602d90cab · outbound

This paper cites Toward advanc ed UA V communications: Properties, research challenges, and future potential,.

Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach Toward advanc ed UA V communications: Properties, research challenges, and future potential,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:39:28.298276Z

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Observation 5f7d216b-334a-49e3-b0e3-c2e006e32071 · outbound

This paper cites High-performance UA V crowdsensing: A deep reinfor ce- ment learning approach,.

Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach High-performance UA V crowdsensing: A deep reinfor ce- ment learning approach,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:39:28.288477Z

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Observation a6434314-e670-43be-a203-7eac486c8764 · outbound

This paper cites Delay-sensitive energy-efﬁcient UA V crowdsensing by dee p rein- forcement learning,.

Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach Delay-sensitive energy-efﬁcient UA V crowdsensing by dee p rein- forcement learning,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:39:28.280230Z

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Observation f0ee3203-47bc-4b9b-af4d-e901de191e4b · outbound

This paper cites A UA V-ass isted multi-task allocation method for mobile crowd sensing,.

Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach A UA V-ass isted multi-task allocation method for mobile crowd sensing,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:39:28.271867Z

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Observation c0710473-0d4b-44ff-a6b8-f63d384d45cf · outbound

This paper cites Secure communications for UA V-enabled mobile ed ge computing systems,.

Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach Secure communications for UA V-enabled mobile ed ge computing systems,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:39:28.262308Z

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Observation f9cba090-9511-4e5c-9df4-82ed3acff409 · outbound

This paper cites Multi-UA V trajectory and power optimization for cached UA V wireless networks with energy a nd content recharging-demand driven deep learning approach,.

Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach Multi-UA V trajectory and power optimization for cached UA V wireless networks with energy a nd content recharging-demand driven deep learning approach,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:39:28.253349Z

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Observation af17a368-feaa-470c-9ad9-3df988d1b4e6 · outbound

This paper cites UA V trajectory optimization for data ofﬂoading at t he edge of multiple cells,.

Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach UA V trajectory optimization for data ofﬂoading at t he edge of multiple cells,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:39:28.243588Z

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Observation 306c3d43-6318-43a3-b0ab-530f0fcb89a5 · outbound

This paper cites Distributed energy -efﬁcient multi-UA V navigation for long-term communication coverag e by deep reinforcement learning,.

Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach Distributed energy -efﬁcient multi-UA V navigation for long-term communication coverag e by deep reinforcement learning,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:39:28.234152Z

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Observation cad0c3cc-eabc-431c-a47a-10ac1aebeb69 · outbound

This paper cites Mu lti- UA V trajectory planning for energy-efﬁcient content cover age: A decentralized learning-based approach,.

Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach Mu lti- UA V trajectory planning for energy-efﬁcient content cover age: A decentralized learning-based approach,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:39:28.224984Z

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Observation 79b94429-5917-45c8-b868-96dc5a75b9e8 · outbound

This paper cites Collaborative computation ofﬂoading and resou rce allocation in multi-UA V-assisted IoT networks: A deep reinforcement learning approach,.

Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach Collaborative computation ofﬂoading and resou rce allocation in multi-UA V-assisted IoT networks: A deep reinforcement learning approach,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:39:28.215463Z

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Observation 3317a431-94d1-430c-a64d-621b7abe1f48 · outbound

This paper cites Path pla nning for UA V-mounted mobile edge computing with deep reinforcem ent learning,.

Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach Path pla nning for UA V-mounted mobile edge computing with deep reinforcem ent learning,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:39:28.206028Z

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Observation 3e57fdc6-bdff-4f62-997c-65ece6ae6c18 · outbound

This paper cites 3D UA V trajectory design and frequency band allocation for energy-efﬁcient and fair com munica- tion: A deep reinforcement learning approach,.

Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach 3D UA V trajectory design and frequency band allocation for energy-efﬁcient and fair com munica- tion: A deep reinforcement learning approach,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:39:28.197866Z

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Observation 10a19e40-e669-42ec-a160-0a5f1997c849 · outbound

This paper cites Joint 3D deployment and powe r allocation for UA V -BS: A deep reinforcement learning appro ach,.

Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach Joint 3D deployment and powe r allocation for UA V -BS: A deep reinforcement learning appro ach,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:39:28.189558Z

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Observation 463bca72-05ed-460e-99c6-64280dcf1979 · outbound

This paper cites Multi-agent deep reinforcement learning-based trajecto ry planning for multi-UA V assisted mobile edge computing,.

Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach Multi-agent deep reinforcement learning-based trajecto ry planning for multi-UA V assisted mobile edge computing,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:39:28.179417Z

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Observation de5202b8-460c-45cc-b721-cc4027160c0c · outbound

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

Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach Multi- agent DRL for task ofﬂoading and resource allocation in mult i- UA V enabled IoT edge network,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:39:28.169269Z

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Observation 32922647-f7fe-49fb-b893-4b9869f5cf40 · outbound

This paper cites Multi-agent reinforcement learni ng based re- source management in MEC- and UA V-assisted vehicular netwo rks,.

Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach Multi-agent reinforcement learni ng based re- source management in MEC- and UA V-assisted vehicular netwo rks,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:39:28.159650Z

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Observation 908101fe-9a5c-4253-b5ca-9ff8066198cd · outbound

This paper cites Multi-agent reinfo rcement learning-based resource allocation for UA V networks,.

Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach Multi-agent reinfo rcement learning-based resource allocation for UA V networks,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:39:28.149872Z

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Observation 4fc34b6a-8cd3-4500-9118-29b11a792ddf · outbound

This paper cites Multiagent collaborative learning for UA V enabl ed wire- less networks,.

Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach Multiagent collaborative learning for UA V enabl ed wire- less networks,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:39:28.140133Z

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Observation 6a1b3ce7-99e8-4d2f-ba10-3e5023eb7738 · outbound

This paper cites Interferenc e management for cellular-connected UA Vs: A deep reinforcement learnin g ap- proach,.

Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach Interferenc e management for cellular-connected UA Vs: A deep reinforcement learnin g ap- proach,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:39:28.130792Z

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Observation a6b6cbc0-e451-4874-a90c-25bd0c7b702e · outbound

This paper cites Downlink power control i n self- organizing dense small cells underlaying macrocells: A mea n ﬁeld game,.

Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach Downlink power control i n self- organizing dense small cells underlaying macrocells: A mea n ﬁeld game,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:39:28.121374Z

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Observation 0176da80-aa41-4193-a787-f5ad47446b2b · outbound

This paper cites Delay optimization in multi-UA V edge caching netwo rks: A robust mean ﬁeld game,.

Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach Delay optimization in multi-UA V edge caching netwo rks: A robust mean ﬁeld game,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:39:28.112740Z

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Observation 9e2f04a5-e82b-40be-a8da-5292b2311362 · outbound

This paper cites Multi- UA V delay optimization in edge caching networks: A mean ﬁeld gam e approach,.

Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach Multi- UA V delay optimization in edge caching networks: A mean ﬁeld gam e approach,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:39:28.103646Z

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Observation 98b4e0df-4a58-42d1-a0fc-28c5ef9c0835 · outbound

This paper cites Mean-ﬁeld game theory based altitude control strategy for massive UA V relay- assisted mobile edge computing,.

Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach Mean-ﬁeld game theory based altitude control strategy for massive UA V relay- assisted mobile edge computing,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:39:28.093984Z

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Observation 7cd64baa-3827-4af0-b420-ea3d65fd9b71 · outbound

This paper cites Massive UA V-to - ground communication and its stable movement control: A mea n- ﬁeld approach,.

Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach Massive UA V-to - ground communication and its stable movement control: A mea n- ﬁeld approach,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:39:28.084694Z

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Observation 052435ef-c1cf-4ce5-8808-b19851507c5d · outbound

This paper cites Joint power control and scheduling for high-dyn amic multi-hop UA V communication: A robust mean ﬁeld game,.

Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach Joint power control and scheduling for high-dyn amic multi-hop UA V communication: A robust mean ﬁeld game,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:39:28.075563Z

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Observation 0353bb19-8c51-4a7b-acfc-5f08097c2ecc · outbound

This paper cites Mea n ﬁeld multi-agent reinforcement learning,.

Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach Mea n ﬁeld multi-agent reinforcement learning,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:39:28.066043Z

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Observation 3ad4ac31-65d6-4c12-88f5-f49b846fb66d · outbound

This paper cites Approximately solving mean ﬁeld g ames via entropy-regularized deep reinforcement learning,.

Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach Approximately solving mean ﬁeld g ames via entropy-regularized deep reinforcement learning,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:39:28.056956Z

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Observation f0bef96e-f484-4870-86b4-306846a3a4fd · outbound

This paper cites Learning mean-ﬁeld ga mes,.

Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach Learning mean-ﬁeld ga mes,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:39:28.047770Z

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Observation 4747413f-6827-400d-963e-472a5e5185ec · outbound

This paper cites Downlink tra nsmit power control in ultra-dense UA V network based on mean ﬁeld g ame and deep reinforcement learning,.

Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach Downlink tra nsmit power control in ultra-dense UA V network based on mean ﬁeld g ame and deep reinforcement learning,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:39:28.038163Z

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Observation 86ca5fbf-1fbd-48c1-96d9-74814c18f18e · outbound

This paper cites Me an ﬁeld deep reinforcement learning for fair and efﬁcient UA V c ontrol,.

Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach Me an ﬁeld deep reinforcement learning for fair and efﬁcient UA V c ontrol,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:39:28.029976Z

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Observation fcb4ed8c-f873-4efb-82b2-69c5b42d99ab · outbound

This paper cites Chase or wait: Dynamic UA V deployme nt to learn and catch time-varying user activities,.

Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach Chase or wait: Dynamic UA V deployme nt to learn and catch time-varying user activities,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:39:28.021846Z

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Observation 7719c77d-4936-4168-b48e-114fdaf3c5c5 · outbound

This paper cites Energy minimization for wi reless communication with rotary-wing UA V,.

Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach Energy minimization for wi reless communication with rotary-wing UA V,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:39:28.012705Z

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Observation e8aedc46-5f2d-4ec4-bc1b-6ffe9c89a862 · outbound

This paper cites Decen tralized federated reinforcement learning for user-centric dynami c TFDD control,.

Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach Decen tralized federated reinforcement learning for user-centric dynami c TFDD control,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:39:28.003271Z

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Observation d26a8c0d-11f9-4185-aab1-662b3bb06d7f · outbound

This paper cites Downlink coverage and rate analysis of an aerial user in ver tical heterogeneous networks (VHetNets),.

Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach Downlink coverage and rate analysis of an aerial user in ver tical heterogeneous networks (VHetNets),

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:39:27.992196Z

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Observation 96c00aae-32be-4b42-b185-82b91339ed1b · outbound

This paper cites Reinfor cement learning with deep energy-based policies,.

Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach Reinfor cement learning with deep energy-based policies,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:39:27.981916Z

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Observation 27c126c7-f9bc-4b95-ae97-f22535d66313 · outbound

This paper cites Adaptive deployment for UA V- aided communication networks,.

Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach Adaptive deployment for UA V- aided communication networks,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:39:27.972050Z

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Observation 137d8e92-6623-4d21-ae4a-61ef31da9dd6 · outbound

This paper cites Probabilistic caching for small-cell networks with terrestrial and aerial users,.

Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach Probabilistic caching for small-cell networks with terrestrial and aerial users,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:39:27.962193Z

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Observation 37231090-c637-4579-b7b0-f93d4f5d3fea · outbound

This paper cites Decision transformers for wireless communications : A new paradigm of resource management,.

Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach Decision transformers for wireless communications : A new paradigm of resource management,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:39:27.952286Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e7d8c27d-1dc9-4190-a7cc-9d7e3cff3c88 · outbound

This paper cites Large popula tion stochastic dynamic games: Closed-loop McKean-Vlasov syst ems and the Nash certainty equivalence principle,.

Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach Large popula tion stochastic dynamic games: Closed-loop McKean-Vlasov syst ems and the Nash certainty equivalence principle,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:39:27.942204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers