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Paper Citation Record · LEDGER

Multi-Agent DRL for QoS and Energy Optimization in RIS-Enabled Open-RAN Industrial 6G TN/NTN Networks

As of 22 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2606.28339.

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

pith.paper-citation-record.v1
2606.28339 v1

Coverage vector

measured 15 of 15 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-30T11:22:58.667118Z

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

15 of 15 outbound references displayed

  • verified exact0
  • verified fuzzy15
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 95bde11b-64bf-4782-a801-a86830168997 · outbound

This paper cites 6g-enabled ultra-reliable low latency communication for industry 5.0: Challenges and future directions,.

Multi-Agent DRL for QoS and Energy Optimization in RIS-Enabled Open-RAN Industrial 6G TN/NTN Networks 6g-enabled ultra-reliable low latency communication for industry 5.0: Challenges and future directions,

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T10:36:10.769631Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-06-30T11:22:58.667118Z digest=sha256:574378b24fdd1710f16ff049ccef78c59d0ac1166da46a387e625f70f5a91f21

Observation efffd2b1-54a3-40d8-8cc1-7f2c6757bb4f · outbound

This paper cites A survey of intelligent network slicing management for industrial iot: Integrated approaches for smart transportation, smart energy, and smart factory,.

Multi-Agent DRL for QoS and Energy Optimization in RIS-Enabled Open-RAN Industrial 6G TN/NTN Networks A survey of intelligent network slicing management for industrial iot: Integrated approaches for smart transportation, smart energy, and smart factory,

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T10:36:10.767727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-06-30T11:22:58.667118Z digest=sha256:2ba9d3dd10564b832e6f447f522cd9f46eb18ae6a4ee6a94e540ed70055d921f

Observation ba8334a4-99a6-41ec-88ea-8767a4d1d5a9 · outbound

This paper cites Empowering traffic steering in 6G Open RAN with deep reinforcement learning,.

Multi-Agent DRL for QoS and Energy Optimization in RIS-Enabled Open-RAN Industrial 6G TN/NTN Networks Empowering traffic steering in 6G Open RAN with deep reinforcement learning,

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T10:36:10.775251Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-06-30T11:22:58.667118Z digest=sha256:484ddb2cc2fa555485def8967190268373c767dd0bef77c0c4d8a104178038ab

Observation 2eb32728-0bb2-46c3-811c-5f9dc40468d4 · outbound

This paper cites Emerging technologies for 6G non-terrestrial-networks: From academia to industrial applications,.

Multi-Agent DRL for QoS and Energy Optimization in RIS-Enabled Open-RAN Industrial 6G TN/NTN Networks Emerging technologies for 6G non-terrestrial-networks: From academia to industrial applications,

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T10:36:10.777442Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-06-30T11:22:58.667118Z digest=sha256:42e08bb4bb697df7a7d8c67b76f769bf6404e655b96bec51d159e5e48f2fac3e

Observation b6da969d-02b3-4385-b0c5-38206ecf58df · outbound

This paper cites Multi-UA V Multi-RIS QoS-Aware Aerial Communication Systems Using DRL and PSO,.

Multi-Agent DRL for QoS and Energy Optimization in RIS-Enabled Open-RAN Industrial 6G TN/NTN Networks Multi-UA V Multi-RIS QoS-Aware Aerial Communication Systems Using DRL and PSO,

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T10:36:10.765338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-06-30T11:22:58.667118Z digest=sha256:1a6f4b32fd7df742a668430085ae31bd605a6afd17f891431a34a11c84a8be41

Observation 80b6fd89-4dd8-4ae1-805f-c833aae839df · outbound

This paper cites Guest editorial xURLLC in 6G: Next generation ultra-reliable and low-latency communications,.

Multi-Agent DRL for QoS and Energy Optimization in RIS-Enabled Open-RAN Industrial 6G TN/NTN Networks Guest editorial xURLLC in 6G: Next generation ultra-reliable and low-latency communications,

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T10:36:10.787697Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-06-30T11:22:58.667118Z digest=sha256:b4ef5631a6f0a78ed07155b2ed672f8f406e25711acbe7661c2964acff425341

Observation ddeb4cf3-e610-462e-ab66-08d6b46630eb · outbound

This paper cites RIS-Enabled UA V Swarm Optimization Framework for Energy Harvesting and Data Collection in Post-Disaster Recovery Management,.

Multi-Agent DRL for QoS and Energy Optimization in RIS-Enabled Open-RAN Industrial 6G TN/NTN Networks RIS-Enabled UA V Swarm Optimization Framework for Energy Harvesting and Data Collection in Post-Disaster Recovery Management,

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T10:36:10.789880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-06-30T11:22:58.667118Z digest=sha256:24ad31d9624b904e3a577ade96b56112716dfd5fa86b4e784748220a751cc97f

Observation ae4b64be-e8aa-4278-a8db-36ba44983eb5 · outbound

This paper cites AoI-Aware Intelligent Platform for Energy and Rate Management in Multi-UA V Multi-RIS System,.

Multi-Agent DRL for QoS and Energy Optimization in RIS-Enabled Open-RAN Industrial 6G TN/NTN Networks AoI-Aware Intelligent Platform for Energy and Rate Management in Multi-UA V Multi-RIS System,

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T10:36:10.785995Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-06-30T11:22:58.667118Z digest=sha256:1f049790eb1dab23f3e5951ebff8cca1022c7c02f9f8655b7c396fddf7392df0

Observation 2acc884f-3b7f-41f6-8022-94f3e47125b7 · outbound

This paper cites Reconfigurable intelligent surfaces for 6g non- terrestrial networks: Assisting connectivity from the sky,.

Multi-Agent DRL for QoS and Energy Optimization in RIS-Enabled Open-RAN Industrial 6G TN/NTN Networks Reconfigurable intelligent surfaces for 6g non- terrestrial networks: Assisting connectivity from the sky,

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T10:36:10.758497Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-06-30T11:22:58.667118Z digest=sha256:88c82251204fa76ddd2a330249a4fb5e8205133962a94e9a3d7bb73cd9e20f7b

Observation 086d01d4-1b6a-40eb-85e0-082c8518a3b7 · outbound

This paper cites Multi-Agent Meta Reinforcement Learning for Reliable and Low-Latency Distributed Inference in Resource-Constrained UA V Swarms,.

Multi-Agent DRL for QoS and Energy Optimization in RIS-Enabled Open-RAN Industrial 6G TN/NTN Networks Multi-Agent Meta Reinforcement Learning for Reliable and Low-Latency Distributed Inference in Resource-Constrained UA V Swarms,

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T10:36:10.760862Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-06-30T11:22:58.667118Z digest=sha256:3c43e3e510dd199c8edeba78bf90bf0dff5ef9c3e5ad8bcb179a93fe13c3e8dd

Observation 844c1056-822a-479f-aa6c-73099a2437cd · outbound

This paper cites Ris- assisted physical-layer key generation for d2d communications with correlated and imperfect channels,.

Multi-Agent DRL for QoS and Energy Optimization in RIS-Enabled Open-RAN Industrial 6G TN/NTN Networks Ris- assisted physical-layer key generation for d2d communications with correlated and imperfect channels,

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T10:36:10.784187Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-06-30T11:22:58.667118Z digest=sha256:03ee10a021539d481dd5e9d5efc714e2b2742014fa5dbd4e644764f3d818e3c7

Observation ab684d95-5bf6-4570-987b-502d212e93c5 · outbound

This paper cites Distributed and secure spectrum sharing for 5g and 6g networks,.

Multi-Agent DRL for QoS and Energy Optimization in RIS-Enabled Open-RAN Industrial 6G TN/NTN Networks Distributed and secure spectrum sharing for 5g and 6g networks,

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T10:36:10.773323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-06-30T11:22:58.667118Z digest=sha256:a49f49226f45cc612eedc75ddad721e199638339d80954b4b99fee386a1da24a

Observation eceed672-ba49-48f3-9f47-f62657a9510f · outbound

This paper cites DRL- Based Joint Resource Allocation and Platoon Control Optimization for UA V-Hosted Platoon Digital Twin,.

Multi-Agent DRL for QoS and Energy Optimization in RIS-Enabled Open-RAN Industrial 6G TN/NTN Networks DRL- Based Joint Resource Allocation and Platoon Control Optimization for UA V-Hosted Platoon Digital Twin,

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T10:36:10.782323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-06-30T11:22:58.667118Z digest=sha256:e97cd56b13ef00403e78f76864279f8cbe425074dbfb900a126a07852f0f580b

Observation 312b99f4-70a7-417e-94f2-23adcae662f0 · outbound

This paper cites Reinforcement learning in multiple-uav networks: Deployment and movement design,.

Multi-Agent DRL for QoS and Energy Optimization in RIS-Enabled Open-RAN Industrial 6G TN/NTN Networks Reinforcement learning in multiple-uav networks: Deployment and movement design,

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T10:36:10.771590Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-06-30T11:22:58.667118Z digest=sha256:f41c0c594c0fef2f2be87a42c6d11d03cdeff353985263dc7c62f83d7db61192

Observation 8d6d04b3-aa00-4bb4-aaa9-f63c4f095ead · outbound

This paper cites Two- tier resource allocation for multitenant network slicing: A federated deep reinforcement learning approach,.

Multi-Agent DRL for QoS and Energy Optimization in RIS-Enabled Open-RAN Industrial 6G TN/NTN Networks Two- tier resource allocation for multitenant network slicing: A federated deep reinforcement learning approach,

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T10:36:10.762704Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-06-30T11:22:58.667118Z digest=sha256:07ce88c69327700257d744a2ba0a69c9e6f0d1605bc721c6238f6c976c4b6c11

Pith citing papers

No inbound Pith citation observations are available.