Paper Citation Record · LEDGER

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning

As of 19 August 2026, this Paper Citation Record lists 65 of 65 outbound references and 2 inbound Pith citation observations for arXiv:2502.05824.

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

pith.paper-citation-record.v1
2502.05824 v1

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Pith citing papers itemized under the disclosed page cap.

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

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Reference resolution

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External citation measurements

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Outbound references

Observation 68a736d4-4205-4981-83b1-6748ea936beb · outbound

This paper cites UAV-enabled secure communications via collaborative beamforming with imperfect eavesdropper information,.

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning UAV-enabled secure communications via collaborative beamforming with imperfect eavesdropper information,

Reference 1

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Observation f97da010-b68b-4bd2-a5f4-d233b07f98e4 · outbound

This paper cites Multiobjective optimization approach for reducing hovering and motion energy consumptions in UAV-assisted collaborative beamforming,.

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning Multiobjective optimization approach for reducing hovering and motion energy consumptions in UAV-assisted collaborative beamforming,

Reference 2

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Observation 95454bc4-9ff3-434a-899f-8b89346b50ad · outbound

This paper cites UAV swarm-enabled collaborative secure relay commu- nications with time-domain colluding eavesdropper,.

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning UAV swarm-enabled collaborative secure relay commu- nications with time-domain colluding eavesdropper,

Reference 3

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Observation c2019144-19bc-4d94-86d5-9e3e12292920 · outbound

This paper cites Accessing from the sky: A tutorial on UAV communications for 5g and beyond,.

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning Accessing from the sky: A tutorial on UAV communications for 5g and beyond,

Reference 4

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Observation 0f908346-e6a9-4b03-b825-bb074cd43a98 · outbound

This paper cites Distributed and collaborative beamforming in wireless sensor networks: Clas- sifications, trends, and research directions,.

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning Distributed and collaborative beamforming in wireless sensor networks: Clas- sifications, trends, and research directions,

Reference 5

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Observation 55788f56-0907-4713-8695-efbc1ed6949b · outbound

This paper cites Optimal positioning of flying base stations and transmission power allocation in NOMA networks,.

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning Optimal positioning of flying base stations and transmission power allocation in NOMA networks,

Reference 6

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Observation 410be8a5-0750-4e6f-8b59-dc677d1395d4 · outbound

This paper cites Rendezvous: Opportunistic data delivery to mobile users by uavs through target trajectory pre- diction,.

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning Rendezvous: Opportunistic data delivery to mobile users by uavs through target trajectory pre- diction,

Reference 7

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Observation 07493e69-b17a-4cd9-8c45-60b6bab4e32f · outbound

This paper cites Privacy-aware and security-enhanced efficient matchmaking en- cryption,.

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning Privacy-aware and security-enhanced efficient matchmaking en- cryption,

Reference 8

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Observation f96844e3-9c6a-4bfe-8a6e-431490bbf80a · outbound

This paper cites Privacy-preserving fine-grained data sharing with dynamic ser- vice for the cloud-edge IoT,.

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning Privacy-preserving fine-grained data sharing with dynamic ser- vice for the cloud-edge IoT,

Reference 9

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Observation e905a571-6923-458c-9fe2-6b57d4491a9d · outbound

This paper cites Multi-objective op- timization for UAV swarm-assisted iot with virtual an- tenna arrays,.

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning Multi-objective op- timization for UAV swarm-assisted iot with virtual an- tenna arrays,

Reference 10

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Observation e381c172-4887-44b8-bb8e-2f2de0d856ff · outbound

This paper cites Secure and energy-efficient UAV relay communications exploiting collabora- tive beamforming,.

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning Secure and energy-efficient UAV relay communications exploiting collabora- tive beamforming,

Reference 11

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This paper cites Optimization design of col- laborative beamforming for heterogeneous UAV swarm,.

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning Optimization design of col- laborative beamforming for heterogeneous UAV swarm,

Reference 12

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Observation 616211b0-adb6-4f52-9d00-7e66a02a3968 · outbound

This paper cites Robust resource allocation algorithm for energy-harvesting-based D2D communication un- derlaying UAV-assisted networks,.

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning Robust resource allocation algorithm for energy-harvesting-based D2D communication un- derlaying UAV-assisted networks,

Reference 13

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Observation e8941548-56b7-4105-9e36-d2b05cfe2cf4 · outbound

This paper cites Coverage control for UAV swarm communication networks: A distributed learning approach,.

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning Coverage control for UAV swarm communication networks: A distributed learning approach,

Reference 14

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Observation c391530a-1c9e-4fcc-b375-7a6eb54523df · outbound

This paper cites A UAV-mounted free space optical communication: Trajectory optimization for flight time,.

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning A UAV-mounted free space optical communication: Trajectory optimization for flight time,

Reference 15

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Observation 34f291d8-5db7-4ba3-b486-65dc22552c15 · outbound

This paper cites Multiuser MISO UAV communications in uncertain environments with no-fly zones: Robust trajectory and resource allocation design,.

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning Multiuser MISO UAV communications in uncertain environments with no-fly zones: Robust trajectory and resource allocation design,

Reference 16

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Observation 08c55de6-5491-4f5f-b618-b20a5be6b611 · outbound

This paper cites A game theory approach for joint access selection and resource allocation in UAV assisted iot communication networks,.

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning A game theory approach for joint access selection and resource allocation in UAV assisted iot communication networks,

Reference 17

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Observation 1efa78d8-4534-4b0b-9815-22ad3147e33f · outbound

This paper cites A stochastic game approach for collaborative beamforming in sdn-based energy har- vesting wireless sensor networks,.

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning A stochastic game approach for collaborative beamforming in sdn-based energy har- vesting wireless sensor networks,

Reference 18

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Observation 04e5df4a-dd73-4740-a727-d775b9b0bfda · outbound

This paper cites Communi- cations and control for wireless drone-based antenna array,.

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning Communi- cations and control for wireless drone-based antenna array,

Reference 19

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Observation 7e0306c7-9a7a-41e2-973b-d6dc676f219f · outbound

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Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning Security energy efficiency analysis of analog collaborative beamforming with stochastic virtual antenna array of UAV swarm,

Reference 20

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This paper cites Resource management of heterogeneous cellular networks with hybrid energy supplies: A multi-objective optimization approach,.

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning Resource management of heterogeneous cellular networks with hybrid energy supplies: A multi-objective optimization approach,

Reference 21

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Observation 0ae9d39b-61e5-4f8f-b253-5a5ac41191b2 · outbound

This paper cites Performance trade-off in UAV-aided wireless- powered communication networks via multi-objective optimiza- tion,.

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning Performance trade-off in UAV-aided wireless- powered communication networks via multi-objective optimiza- tion,

Reference 22

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Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning End-to-end energy- efficiency and reliability of UAV-assisted wireless data ferrying,

Reference 23

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Observation 50a99ad7-7cf0-4215-9cb9-ad7c3e59a0f2 · outbound

This paper cites Aoi-energy-aware UAV- assisted data collection for iot networks: A deep reinforcement learning method,.

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning Aoi-energy-aware UAV- assisted data collection for iot networks: A deep reinforcement learning method,

Reference 24

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Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning Multi-agent reinforcement learning-based resource allocation for UAV networks,

Reference 25

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Observation f5e0a087-4f17-4df9-bc4a-2cc14c363a64 · outbound

This paper cites Energy-efficient UAV-enabled data collection via wireless charg- ing: A reinforcement learning approach,.

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning Energy-efficient UAV-enabled data collection via wireless charg- ing: A reinforcement learning approach,

Reference 26

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Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning Simultaneous navigation and radio mapping for cellular-connected UAV with deep rein- forcement learning,

Reference 27

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This paper cites Multi-agent deep reinforcement learning for task offloading in UAV-assisted mobile edge computing,.

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning Multi-agent deep reinforcement learning for task offloading in UAV-assisted mobile edge computing,

Reference 28

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Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning Collaborative ground-space communications via evolutionary multi-objective deep reinforcement learning,

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Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning A tutorial on UAVs for wireless networks: Applications, challenges, and open problems,

Reference 30

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Observation 8bc88c2d-88ce-4727-9813-7416e7c9f1ef · outbound

This paper cites Ultra reliable UAV communication using altitude and cooperation diversity,.

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning Ultra reliable UAV communication using altitude and cooperation diversity,

Reference 31

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Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning Coordinated beamforming for the multi- cell multi-antenna wireless system,

Reference 32

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Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning Wide- band inter-beam interference cancellation for mmW/Sub-THz phased arrays with squint,

Reference 33

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Observation e5e5381c-c251-482b-8462-c58bfe21398b · outbound

This paper cites Joint 3d maneuver and power adaptation for secure UAV communication with comp reception,.

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning Joint 3d maneuver and power adaptation for secure UAV communication with comp reception,

Reference 34

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Observation 76107744-88c5-4d9d-b1f5-72ae3976daa7 · outbound

This paper cites Energy minimization for wireless communication with rotary-wing UAV,.

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning Energy minimization for wireless communication with rotary-wing UAV,

Reference 35

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Observation b9ad6010-91a1-4ca1-b4c1-bdbc6daf2773 · outbound

This paper cites Fundamentals of mobility-aware performance characterization of cellular networks: A tutorial,.

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning Fundamentals of mobility-aware performance characterization of cellular networks: A tutorial,

Reference 36

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Observation 86c9a566-466f-4cc5-a459-e68bbb03de5d · outbound

This paper cites Localized weighted sum method for many-objective optimization,.

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning Localized weighted sum method for many-objective optimization,

Reference 37

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Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning A computational approach based on the ε-constraint method in multi-objective optimization prob- lems,

Reference 38

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Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning Exhaustive search, combinatorial optimization and enumeration: Exploring the potential of raw computing power,

Reference 39

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Observation c3b70ec7-d687-4744-8bb6-f2d5abb915d3 · outbound

This paper cites Boyd and L.

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning Boyd and L

Reference 40

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Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning An evolutionary algorithm approach to link prediction in dynamic social networks,

Reference 41

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This paper cites Evolutionary algorithm based offline/online path plan- ner for UAV navigation,.

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning Evolutionary algorithm based offline/online path plan- ner for UAV navigation,

Reference 42

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Observation 0a29f446-96ed-4c84-9d2c-2aa9e180ad17 · outbound

This paper cites Deep reinforce- ment learning for multiagent systems: A review of challenges, solutions, and applications,.

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning Deep reinforce- ment learning for multiagent systems: A review of challenges, solutions, and applications,

Reference 43

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Observation 29bab224-44b1-4991-a1db-82d251c1187f · outbound

This paper cites Real-time optimal energy management of microgrid with uncertainties based on deep reinforcement learning,.

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning Real-time optimal energy management of microgrid with uncertainties based on deep reinforcement learning,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:54:50.642669Z

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Observation c03b599a-cdf5-498e-92ed-75733c0e6c7f · outbound

This paper cites Intelligent integrated sensing and communication: a survey,.

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning Intelligent integrated sensing and communication: a survey,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:54:50.626006Z

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Observation 030d20d7-e93f-4b24-a2e1-77a33c5e7bf9 · outbound

This paper cites Prediction- guided multi-objective reinforcement learning for continuous robot control,.

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning Prediction- guided multi-objective reinforcement learning for continuous robot control,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:54:50.609630Z

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Observation 522baf27-a624-42eb-9b03-190883efba42 · outbound

This paper cites Three-dimension trajectory design for multi-UAV wireless network with deep rein- forcement learning,.

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning Three-dimension trajectory design for multi-UAV wireless network with deep rein- forcement learning,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:54:50.594061Z

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Observation fb45f5ef-fce8-402d-9d90-a15874a10a9b · outbound

This paper cites 3D-trajectory and phase- shift design for RIS-assisted UAV systems using deep reinforce- ment learning,.

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning 3D-trajectory and phase- shift design for RIS-assisted UAV systems using deep reinforce- ment learning,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:54:50.577865Z

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Observation 2a3b5a29-3f8c-4b70-9122-47fdcd4b0f2b · outbound

This paper cites Multi-UAV path planning for wireless data harvesting with deep reinforce- ment learning,.

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning Multi-UAV path planning for wireless data harvesting with deep reinforce- ment learning,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:54:50.562132Z

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Observation 44e337ed-3db0-4fab-8be2-52e1dfba5e09 · outbound

This paper cites Path planning for UAV ground target tracking via deep reinforcement learning,.

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning Path planning for UAV ground target tracking via deep reinforcement learning,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:54:50.544691Z

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Observation eaa5ca20-eb00-4502-8acf-8e4041f91e12 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning Proximal Policy Optimization Algorithms

Reference 51

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Observation 515ea6bc-3e4f-429c-a216-bd0043455d30 · outbound

This paper cites Physical layer secure communications based on collaborative beamforming for UAV networks: A multi-objective optimization approach,.

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning Physical layer secure communications based on collaborative beamforming for UAV networks: A multi-objective optimization approach,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:54:50.528727Z

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Observation 6e4b8c72-cfaa-4f60-8902-86e42bd6e677 · outbound

This paper cites Information-aware driven dynamic LEO-RAN slicing algorithm joint with commu- 18 nication, computing, and caching,.

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning Information-aware driven dynamic LEO-RAN slicing algorithm joint with commu- 18 nication, computing, and caching,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:54:50.513039Z

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Observation d060e24c-cf11-48f5-9d07-4f6f9764178d · outbound

This paper cites Evolutionary multi-objective reinforcement learning based trajectory control and task offloading in UAV-assisted mo- bile edge computing,.

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning Evolutionary multi-objective reinforcement learning based trajectory control and task offloading in UAV-assisted mo- bile edge computing,

Reference 54

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-08T17:54:49.919296Z

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Observation 66acadc2-0dd8-42a5-af74-3cd8108f51e3 · outbound

This paper cites Neural combinatorial deep reinforcement learning for age- optimal joint trajectory and scheduling design in uav-assisted networks,.

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning Neural combinatorial deep reinforcement learning for age- optimal joint trajectory and scheduling design in uav-assisted networks,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:54:50.497716Z

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Observation cfc8a960-e822-4642-8fcc-ed611fe08fae · outbound

This paper cites Interactive exploration of design trade-offs,.

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning Interactive exploration of design trade-offs,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:54:50.481801Z

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Observation 0f9f62f6-b0de-42cf-aa66-8bc4bc1e66b1 · outbound

This paper cites Decentralized navigation with heterogeneous federated reinforcement learning for UAV-enabled mobile edge computing,.

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning Decentralized navigation with heterogeneous federated reinforcement learning for UAV-enabled mobile edge computing,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:54:50.465576Z

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Observation 0eec3bba-409e-4a46-9610-7663df7882df · outbound

This paper cites UAV-enabled collaborative beamforming via multi-agent deep reinforcement learning,.

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning UAV-enabled collaborative beamforming via multi-agent deep reinforcement learning,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:54:50.448340Z

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Observation bb19f68b-cf34-4b47-b70a-845410325ad5 · outbound

This paper cites Intelligent adaptive gossip-based broadcast protocol for UAV-MEC using multi-agent deep reinforcement learning,.

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning Intelligent adaptive gossip-based broadcast protocol for UAV-MEC using multi-agent deep reinforcement learning,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:54:50.432501Z

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Observation 2a62769e-ea52-4632-8792-902e9a1daaf5 · outbound

This paper cites Joint optimization of trajectory and jamming power for multiple uav-aided proactive eavesdropping,.

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning Joint optimization of trajectory and jamming power for multiple uav-aided proactive eavesdropping,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:54:50.416452Z

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Observation 30949b71-8174-450a-97d4-856ed066450b · outbound

This paper cites A comprehensive survey on transfer learning,.

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning A comprehensive survey on transfer learning,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:54:50.399967Z

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Observation ee76f1f5-9c7b-4f93-b3df-691f9061f36b · outbound

This paper cites A grid- based inverted generational distance for multi/many-objective optimization,.

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning A grid- based inverted generational distance for multi/many-objective optimization,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:54:50.384338Z

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Observation 9b27da50-784d-4017-ba5a-fa26c1c61a78 · outbound

This paper cites A survey on the hypervolume indicator in evolutionary multiobjective optimiza- tion,.

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning A survey on the hypervolume indicator in evolutionary multiobjective optimiza- tion,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:54:50.368376Z

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Observation 384ba16e-b85a-4142-9508-f56051ecb76e · outbound

This paper cites MOEA/D: A multiobjective evolutionary algorithm based on decomposition,.

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning MOEA/D: A multiobjective evolutionary algorithm based on decomposition,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:54:50.351664Z

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Observation 6b78ad16-014c-4285-bb2a-2cdafac3981a · outbound

This paper cites MOPSO: a proposal for multiple objective particle swarm optimization,.

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning MOPSO: a proposal for multiple objective particle swarm optimization,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:54:50.328088Z

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Pith citing papers

Observation b750f695-4c42-4fd6-bde7-af9667479b62 · inbound

Toward Realization of Low-Altitude Economy Networks: Core Architecture, Integrated Technologies, and Future Directions cites this paper.

Toward Realization of Low-Altitude Economy Networks: Core Architecture, Integrated Technologies, and Future Directions Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning

Reference 31

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Observation df321392-a82a-4dc6-b0ca-32b2f5831d44 · inbound

Joint Resource Management for Energy-efficient UAV-assisted SWIPT-MEC: A Deep Reinforcement Learning Approach cites this paper.

Joint Resource Management for Energy-efficient UAV-assisted SWIPT-MEC: A Deep Reinforcement Learning Approach Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-08-16T00:01:56.296084Z

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