Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-14T14:01:53.325228Z
Paper Citation Record · LEDGER
Online Maneuver Design for UAV-Enabled NOMA Systems via Reinforcement Learning
As of 16 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:1908.03984.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
22 of 22 outbound references displayed
- verified exact0
- verified fuzzy19
- unresolved3
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 29cca99d-3551-4b55-8b77-0a5de1ca750e · outbound
This paper cites Wireless communication s with unmanned aerial vehicles: Opportunities and challenges,.
Online Maneuver Design for UAV-Enabled NOMA Systems via Reinforcement Learning Wireless communication s with unmanned aerial vehicles: Opportunities and challenges,
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:01:53.791249Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation b345abb6-e9df-4706-911a-cb899507647a · outbound
This paper cites Throughput maximizatio n for UA V - enabled mobile relaying systems,.
Online Maneuver Design for UAV-Enabled NOMA Systems via Reinforcement Learning Throughput maximizatio n for UA V - enabled mobile relaying systems,
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:01:53.771784Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 147427be-6457-490c-a71b-aa946915fdf8 · outbound
This paper cites Capacity characterization of UA V -enabled two-user broadcast channel,.
Online Maneuver Design for UAV-Enabled NOMA Systems via Reinforcement Learning Capacity characterization of UA V -enabled two-user broadcast channel,
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:01:53.751713Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 175a1705-0b66-4dc1-9a65-8388feb449ea · outbound
This paper cites Joint trajectory and commun ication design for multi-UA V enabled wireless networks,.
Online Maneuver Design for UAV-Enabled NOMA Systems via Reinforcement Learning Joint trajectory and commun ication design for multi-UA V enabled wireless networks,
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:01:53.736477Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 76037a32-9fe5-4fc6-8698-219c0501effb · outbound
This paper cites UA V communication based on non-orthogonal multiple access,.
Online Maneuver Design for UAV-Enabled NOMA Systems via Reinforcement Learning UA V communication based on non-orthogonal multiple access,
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:01:53.720938Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 196729db-b70c-4535-88b5-b5819209ab24 · outbound
This paper cites Fundamental rate limits of UA V -enabled m ultiple access channel with trajectory optimization.
Online Maneuver Design for UAV-Enabled NOMA Systems via Reinforcement Learning Fundamental rate limits of UA V -enabled m ultiple access channel with trajectory optimization
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:01:53.706113Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation e9472820-f4d1-4d3f-9228-56d2ccc555a0 · outbound
This paper cites Optimal LA P altitude for maximum coverage,.
Online Maneuver Design for UAV-Enabled NOMA Systems via Reinforcement Learning Optimal LA P altitude for maximum coverage,
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:01:53.688840Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 624496e0-6b28-457d-9bbc-0da9ed3e4296 · outbound
This paper cites Placement optimi zation of UA V -mounted mobile base stations,.
Online Maneuver Design for UAV-Enabled NOMA Systems via Reinforcement Learning Placement optimi zation of UA V -mounted mobile base stations,
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:01:53.670855Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation e5b5e4dc-aa8a-430f-a6ec-f189566b26d2 · outbound
This paper cites Placement optimization for UA V -enabled wireless networks with multi-hop backhauls,.
Online Maneuver Design for UAV-Enabled NOMA Systems via Reinforcement Learning Placement optimization for UA V -enabled wireless networks with multi-hop backhauls,
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:01:53.652164Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation dc8d7810-5c5f-4127-b48d-39b02d11e7c1 · outbound
This paper cites Mobile unmanned aerial vehicles (UA Vs) for energy-efficient Internet of thi ngs communi- cation,.
Online Maneuver Design for UAV-Enabled NOMA Systems via Reinforcement Learning Mobile unmanned aerial vehicles (UA Vs) for energy-efficient Internet of thi ngs communi- cation,
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:01:53.633496Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 87cedeee-edaa-499e-80ea-627b06cd6a31 · outbound
This paper cites Throughput maximization fo r UA V - enabled wireless powered communication networks,.
Online Maneuver Design for UAV-Enabled NOMA Systems via Reinforcement Learning Throughput maximization fo r UA V - enabled wireless powered communication networks,
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:01:53.615662Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation d6426fa1-b600-4a50-9101-8736a5c95720 · outbound
This paper cites Cellular-enabled UA V c ommu- nication: A connectivity-constrained trajectory optimiz ation perspec- tive,.
Online Maneuver Design for UAV-Enabled NOMA Systems via Reinforcement Learning Cellular-enabled UA V c ommu- nication: A connectivity-constrained trajectory optimiz ation perspec- tive,
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:01:53.596828Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 8a6b6ca6-6fe6-4d4f-8fc4-e8e6543c1dcd · outbound
This paper cites Deep learning in physical layer communications,.
Online Maneuver Design for UAV-Enabled NOMA Systems via Reinforcement Learning Deep learning in physical layer communications,
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:01:53.577717Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation a758d3a3-3a0b-4f17-ba52-3a4510376f13 · outbound
This paper cites Trajectory optimization for autonomous flying base station via reinforcement learni ng,.
Online Maneuver Design for UAV-Enabled NOMA Systems via Reinforcement Learning Trajectory optimization for autonomous flying base station via reinforcement learni ng,
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:01:53.555168Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation bb78223b-ca1a-4f98-97cf-c3349db39502 · outbound
This paper cites Energy-ef ficient UA V control for effective and fair communication coverage: A deep reinforcement learning approach.
Online Maneuver Design for UAV-Enabled NOMA Systems via Reinforcement Learning Energy-ef ficient UA V control for effective and fair communication coverage: A deep reinforcement learning approach
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:01:53.527793Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 80b754cd-bac3-495b-86eb-aa615e6665bb · outbound
This paper cites Interferenc e management for cellular-connected UA Vs: A deep reinforcement learning approach,.
Online Maneuver Design for UAV-Enabled NOMA Systems via Reinforcement Learning Interferenc e management for cellular-connected UA Vs: A deep reinforcement learning approach,
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:01:53.502501Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 5381cff3-f86c-4df2-bd0a-22b17c9237c5 · outbound
This paper cites Path design for cellular-connected U A V with reinforcement learning,.
Online Maneuver Design for UAV-Enabled NOMA Systems via Reinforcement Learning Path design for cellular-connected U A V with reinforcement learning,
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:01:53.478225Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation cb9f17c5-fade-463e-8b76-5d737848731e · outbound
This paper cites Optimal positioning of flying re lays for wireless networks: A LOS map approach,.
Online Maneuver Design for UAV-Enabled NOMA Systems via Reinforcement Learning Optimal positioning of flying re lays for wireless networks: A LOS map approach,
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:01:53.454850Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation beeedec7-f18d-41ea-9183-a3d407e2cf10 · outbound
This paper cites an unresolved cited work.
Online Maneuver Design for UAV-Enabled NOMA Systems via Reinforcement Learning Unresolved cited work
Reference 19
- Resolution
- unresolved
- raw_fallback, observed 2026-08-14T14:01:53.430496Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 126a65fd-2567-4943-ab98-c244aa3f4ac9 · outbound
This paper cites A review of deep learning methods and applications for unmann ed aerial vehicles,.
Online Maneuver Design for UAV-Enabled NOMA Systems via Reinforcement Learning A review of deep learning methods and applications for unmann ed aerial vehicles,
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:01:53.411450Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation fe9ea78f-4908-47eb-83b1-26daff235a17 · outbound
This paper cites an unresolved cited work.
Online Maneuver Design for UAV-Enabled NOMA Systems via Reinforcement Learning Unresolved cited work
Reference 21
- Resolution
- unresolved
- raw_fallback, observed 2026-08-14T14:01:53.390869Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation b185d0da-7a63-4bce-a382-c227db842d2a · outbound
This paper cites Optimal L AP altitude for maximum coverage,.
Online Maneuver Design for UAV-Enabled NOMA Systems via Reinforcement Learning Optimal L AP altitude for maximum coverage,
Reference 22
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T14:01:53.325228Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
No inbound Pith citation observations are available.