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Paper Citation Record · LEDGER

Percentile-Based Deep Reinforcement Learning and Reward Based Personalization For Delay Aware RAN Slicing in O-RAN

As of 15 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2507.18111.

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

pith.paper-citation-record.v1
2507.18111 v1

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T14:44:09.069227Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+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

44 of 44 outbound references displayed

  • verified exact1
  • verified fuzzy36
  • unresolved4
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b4ffba47-3831-4068-80c2-c7e8e5d418fe · outbound

This paper cites write newline.

Percentile-Based Deep Reinforcement Learning and Reward Based Personalization For Delay Aware RAN Slicing in O-RAN write newline

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-06T14:44:04.724107Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T14:44:04.724107Z digest=sha256:110caaff69fed4eae962656e9a0f710c359f29f14c8b68a79defdad6a922d7ca

Observation a7835e38-4d1f-4c4a-9625-cee7df34c41a · outbound

This paper cites Federated deep reinforcement learning for open ran slicing in 6g networks.

Percentile-Based Deep Reinforcement Learning and Reward Based Personalization For Delay Aware RAN Slicing in O-RAN Federated deep reinforcement learning for open ran slicing in 6g networks

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:44:09.799288Z

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.

source=arxiv_source observed=2026-08-06T14:44:04.853810Z digest=sha256:d1d85aba655861bdaf823f390a318dd13d6eec61802c192448fd46f69ab085b3

Observation 804cdbfa-37a4-43f7-a53c-89dc2fcbe06d · outbound

This paper cites Energy saving and traffic steering use case and testing by o-ran ric xapp/rapp multi-vendor interoperability.

Percentile-Based Deep Reinforcement Learning and Reward Based Personalization For Delay Aware RAN Slicing in O-RAN Energy saving and traffic steering use case and testing by o-ran ric xapp/rapp multi-vendor interoperability

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:44:09.784565Z

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.

source=arxiv_source observed=2026-08-06T14:44:05.107804Z digest=sha256:5866dd683dddec63c6526d9d9858d39eb8a59ca0d93d856e925616d6bd65e0f8

Observation 0aaee55c-71dd-4b81-bfad-4f09dbff082d · outbound

This paper cites A., Ksentini, A., and Bouaziz, M.

Percentile-Based Deep Reinforcement Learning and Reward Based Personalization For Delay Aware RAN Slicing in O-RAN A., Ksentini, A., and Bouaziz, M

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:44:09.768496Z

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.

source=arxiv_source observed=2026-08-06T14:44:05.139145Z digest=sha256:a5c18fc6ee67209bf9a75fb8966112623204c62df2c4e3b927548b0431375921

Observation 21471594-5e8e-49b4-aafe-bf721f932e3a · outbound

This paper cites Scope: An open and softwarized prototyping platform for nextg systems.

Percentile-Based Deep Reinforcement Learning and Reward Based Personalization For Delay Aware RAN Slicing in O-RAN Scope: An open and softwarized prototyping platform for nextg systems

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:44:09.752806Z

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.

source=arxiv_source observed=2026-08-06T14:44:05.215451Z digest=sha256:77ab71c72dce514d06e2aea42e8e4fe78677170a670fe055793de071935fc62a

Observation 331bdcc8-7ffe-4573-9050-cc812c16b3ab · outbound

This paper cites Intelligence and learning in o-ran for data-driven nextg cellular networks.

Percentile-Based Deep Reinforcement Learning and Reward Based Personalization For Delay Aware RAN Slicing in O-RAN Intelligence and learning in o-ran for data-driven nextg cellular networks

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:44:09.738945Z

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.

source=arxiv_source observed=2026-08-06T14:44:05.371790Z digest=sha256:9387434ae9862db30f686d5ec38bc03628fe7501375eaf2f3e4bb46e52cd4baf

Observation 8e38afd4-7bec-4592-b68f-09e72d817fdc · outbound

This paper cites Colosseum: Large-scale wireless experimentation through hardware-in-the-loop network emulation.

Percentile-Based Deep Reinforcement Learning and Reward Based Personalization For Delay Aware RAN Slicing in O-RAN Colosseum: Large-scale wireless experimentation through hardware-in-the-loop network emulation

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:44:09.725060Z

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.

source=arxiv_source observed=2026-08-06T14:44:05.515789Z digest=sha256:1e536fb312c6a21820185229634f3e6eac47b90eb82517421019d97b47f2bc22

Observation dc457f42-d003-40f5-b92f-8da6582521f6 · outbound

This paper cites User access control in open radio access networks: A federated deep reinforcement learning approach.

Percentile-Based Deep Reinforcement Learning and Reward Based Personalization For Delay Aware RAN Slicing in O-RAN User access control in open radio access networks: A federated deep reinforcement learning approach

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:44:09.711556Z

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.

source=arxiv_source observed=2026-08-06T14:44:05.586167Z digest=sha256:fc0b4201b5ef0dab0439de82f0f315876f2b14a0876efc0346fc1c714e1e279b

Observation 5f680ae8-fa67-493c-84cc-7f55cdaeae6a · outbound

This paper cites Orchestran: Network automation through orchestrated intelligence in the open ran.

Percentile-Based Deep Reinforcement Learning and Reward Based Personalization For Delay Aware RAN Slicing in O-RAN Orchestran: Network automation through orchestrated intelligence in the open ran

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:44:09.696370Z

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.

source=arxiv_source observed=2026-08-06T14:44:05.718449Z digest=sha256:5261c7192462861826c80089f749cb9e9fec46d3b1a77d0cc32bf389a7011cec

Observation 133fecd6-7d74-445f-822a-fc511572cdac · outbound

This paper cites E., Jemaa, S.

Percentile-Based Deep Reinforcement Learning and Reward Based Personalization For Delay Aware RAN Slicing in O-RAN E., Jemaa, S

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:44:09.680517Z

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.

source=arxiv_source observed=2026-08-06T14:44:05.842847Z digest=sha256:a806785868d4014152d8e631b1c81c2596e1637c04015e27ca399ef3c168c21e

Observation 825c39b5-6074-48f1-9a65-728721ced8f0 · outbound

This paper cites Radio access network (ran) digital twins.

Percentile-Based Deep Reinforcement Learning and Reward Based Personalization For Delay Aware RAN Slicing in O-RAN Radio access network (ran) digital twins

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:44:09.665391Z

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.

source=arxiv_source observed=2026-08-06T14:44:05.946180Z digest=sha256:64896a57fd33214a2d86e381a043689db44658ff240ceb72d78fc5f060b5724d

Observation 862d8328-2d01-4cfe-83a3-ca6fbc4cd5d6 · outbound

This paper cites Dynamic sdn-based radio access network slicing with deep reinforcement learning for urllc and embb services.

Percentile-Based Deep Reinforcement Learning and Reward Based Personalization For Delay Aware RAN Slicing in O-RAN Dynamic sdn-based radio access network slicing with deep reinforcement learning for urllc and embb services

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:44:09.650934Z

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.

source=arxiv_source observed=2026-08-06T14:44:06.115987Z digest=sha256:70afff5cd5b4d0e2e6234242cfb110350118d81010e67abc8afdb74b4461a191

Observation 5a3496cd-2bd6-4374-80d9-cb8fbd92a003 · outbound

This paper cites Learning based on graph: A joint interference coordination for cluster-wise distributed mu-mimo.

Percentile-Based Deep Reinforcement Learning and Reward Based Personalization For Delay Aware RAN Slicing in O-RAN Learning based on graph: A joint interference coordination for cluster-wise distributed mu-mimo

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:44:09.635398Z

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.

source=arxiv_source observed=2026-08-06T14:44:06.238765Z digest=sha256:94bf9040f88674902309f251e28dd2295e92d9a8fa120134936cc25b3692097b

Observation 94167ac5-ac74-4010-801a-53230f0592fc · outbound

This paper cites Gan-powered deep distributional reinforcement learning for resource management in network slicing.

Percentile-Based Deep Reinforcement Learning and Reward Based Personalization For Delay Aware RAN Slicing in O-RAN Gan-powered deep distributional reinforcement learning for resource management in network slicing

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:44:09.616341Z

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.

source=arxiv_source observed=2026-08-06T14:44:06.337347Z digest=sha256:33e631ab012757d5921329cd09cff95a0169eaabc49eaa44b7d711c242843216

Observation 7692a33d-b293-4927-8ec3-20e827e85f48 · outbound

This paper cites Personalized cross-silo federated learning on non-iid data.

Percentile-Based Deep Reinforcement Learning and Reward Based Personalization For Delay Aware RAN Slicing in O-RAN Personalized cross-silo federated learning on non-iid data

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:44:09.601149Z

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.

source=arxiv_source observed=2026-08-06T14:44:06.437039Z digest=sha256:58e124e0503e969d2ef195549b3a47ab18431e16f2c0b73624ea748846312b89

Observation 12d12af9-4f6e-4c2d-b2b9-1e10b1bec42f · outbound

This paper cites E., and Erol-Kantarci, M.

Percentile-Based Deep Reinforcement Learning and Reward Based Personalization For Delay Aware RAN Slicing in O-RAN E., and Erol-Kantarci, M

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:44:09.586300Z

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.

source=arxiv_source observed=2026-08-06T14:44:06.548601Z digest=sha256:7baefeb3acd8fcc8d884632390298e00ef381d4ff6469edf055285f1c8b9216b

Observation 51038bbc-477d-4d89-8f3c-ca41ccb4425d · outbound

This paper cites and Iosifidis, G.

Percentile-Based Deep Reinforcement Learning and Reward Based Personalization For Delay Aware RAN Slicing in O-RAN and Iosifidis, G

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:44:09.571249Z

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.

source=arxiv_source observed=2026-08-06T14:44:06.674726Z digest=sha256:1019c8829bb0325313ae2a2fbb75f7f767248b7b75b9aebaf3775e1f918b8315

Observation 70b3e45c-93e3-406e-80a2-b74b12e4b6bf · outbound

This paper cites Generative ai in mobile networks: a survey.

Percentile-Based Deep Reinforcement Learning and Reward Based Personalization For Delay Aware RAN Slicing in O-RAN Generative ai in mobile networks: a survey

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:44:09.555603Z

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.

source=arxiv_source observed=2026-08-06T14:44:06.782704Z digest=sha256:8a0716ea6a1d7788ec01addfdf5c89f39ee4ec24174dd24bccfa2b67807c1457

Observation f9fa14d9-71fa-41aa-8316-3a42ff7e4e60 · outbound

This paper cites K., Lazaridis, P.

Percentile-Based Deep Reinforcement Learning and Reward Based Personalization For Delay Aware RAN Slicing in O-RAN K., Lazaridis, P

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:44:09.535433Z

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.

source=arxiv_source observed=2026-08-06T14:44:06.871537Z digest=sha256:28b2d34e30a6df3c38b48e9fbbefbe10a53ba4e71cd3d298a3a9b52d04c5b940

Observation e17ac07b-b57a-44b9-ace5-a69efb3f43ad · outbound

This paper cites Programmable and Customized Intelligence for Traffic Steering in 5G Networks Using Open RAN Architectures.

Percentile-Based Deep Reinforcement Learning and Reward Based Personalization For Delay Aware RAN Slicing in O-RAN Programmable and Customized Intelligence for Traffic Steering in 5G Networks Using Open RAN Architectures

Reference 20

Resolution
metadata mismatch
local_arxiv, observed 2026-08-06T14:44:09.171966Z

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.

source=arxiv_source observed=2026-08-06T14:44:06.991978Z digest=sha256:5e0ed7e686c209aadf744bb55edca338993a543e2cb9e74cb0c70db48ce3ee77

Observation 7f8dd7a3-3e20-4b25-8cba-252eb9aa111d · outbound

This paper cites Session management for urllc in 5g open radio access network: A machine learning approach.

Percentile-Based Deep Reinforcement Learning and Reward Based Personalization For Delay Aware RAN Slicing in O-RAN Session management for urllc in 5g open radio access network: A machine learning approach

Reference 21

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verified fuzzy
raw_fallback, observed 2026-08-06T14:44:09.518414Z

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.

source=arxiv_source observed=2026-08-06T14:44:07.072101Z digest=sha256:fec6e6737aed6df8b4bfd6cfc751cd8e0f106cea187a45604b19ec750c5183f9

Observation e94be4d8-78b2-4d74-846d-4c19a7d98ef4 · outbound

This paper cites an unresolved cited work.

Percentile-Based Deep Reinforcement Learning and Reward Based Personalization For Delay Aware RAN Slicing in O-RAN Unresolved cited work

Reference 22

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unresolved
raw_fallback, observed 2026-08-06T14:44:09.503678Z

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.

source=arxiv_source observed=2026-08-06T14:44:07.174502Z digest=sha256:61d1a3870858800c6f2767fce712e95b774457b5249183823b7c6cbab133dd72

Observation 5d1db9db-b9e5-4b52-9ced-6a848f58bc95 · outbound

This paper cites B., and Abou-Zeid, H.

Percentile-Based Deep Reinforcement Learning and Reward Based Personalization For Delay Aware RAN Slicing in O-RAN B., and Abou-Zeid, H

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:44:09.489459Z

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.

source=arxiv_source observed=2026-08-06T14:44:07.259284Z digest=sha256:4c4924d234829e2e1ffb9d9aecfefb50995f6d40c47b1ac4221053c2352639be

Observation 75be1895-381a-4750-9e0f-3a666c4acf49 · outbound

This paper cites B., Quang, P.

Percentile-Based Deep Reinforcement Learning and Reward Based Personalization For Delay Aware RAN Slicing in O-RAN B., Quang, P

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:44:09.473882Z

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.

source=arxiv_source observed=2026-08-06T14:44:07.384419Z digest=sha256:246266d297affd9807f5cd0463ceeb3d7f177240c5642bb9721377d50cf8f348

Observation fa5be63d-6335-40eb-a664-36793c3cf2d0 · outbound

This paper cites A., Veness, J., Bellemare, M.

Percentile-Based Deep Reinforcement Learning and Reward Based Personalization For Delay Aware RAN Slicing in O-RAN A., Veness, J., Bellemare, M

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-06T14:44:07.475651Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T14:44:07.475651Z digest=sha256:32b2ec8f76ed0b461cfacb69f1ee4dc703ae749fa4f11420e7149017ff84ead3

Observation 4a702cd6-bff6-4ef1-bd96-3fda7b1a7d17 · outbound

This paper cites K., Shah-Mansouri, V., Parsaeefard, S., and L \'o pez, O.

Percentile-Based Deep Reinforcement Learning and Reward Based Personalization For Delay Aware RAN Slicing in O-RAN K., Shah-Mansouri, V., Parsaeefard, S., and L \'o pez, O

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:44:09.441330Z

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.

source=arxiv_source observed=2026-08-06T14:44:07.559850Z digest=sha256:775131d2fccd8df8a4394a3d46ad546a54e750d7dede4b9313a96224e8f8a1fd

Observation 7fc9b5ec-810d-406d-af98-7a17b528eb51 · outbound

This paper cites M., Abou-zeid, H., and Hassanein, H.

Percentile-Based Deep Reinforcement Learning and Reward Based Personalization For Delay Aware RAN Slicing in O-RAN M., Abou-zeid, H., and Hassanein, H

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:44:09.424164Z

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.

source=arxiv_source observed=2026-08-06T14:44:07.644475Z digest=sha256:9c04c031dba1df4e0e323a3cae252398a0970dd100f0d509251b36c378bf9ca8

Observation 92942bcd-f9f1-437b-8cef-b1da9e3b4d8e · outbound

This paper cites S., Singh, S., Banerji, R., Reed, J.

Percentile-Based Deep Reinforcement Learning and Reward Based Personalization For Delay Aware RAN Slicing in O-RAN S., Singh, S., Banerji, R., Reed, J

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:44:09.408687Z

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.

source=arxiv_source observed=2026-08-06T14:44:07.768609Z digest=sha256:0599fee53dc40b29a1978a194694cf6becc30bc221f9591b1c3bc2284b4c2cb8

Observation b5e170c2-0c18-4599-95b2-e0520ccb697c · outbound

This paper cites O-RAN Architecture Description 10.0.

Percentile-Based Deep Reinforcement Learning and Reward Based Personalization For Delay Aware RAN Slicing in O-RAN O-RAN Architecture Description 10.0

Reference 29

Resolution
malformed identifier
raw_fallback, observed 2026-08-06T14:44:09.392497Z

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.

source=arxiv_source observed=2026-08-06T14:44:07.891437Z digest=sha256:758272b357df39a84dbb259849c3c80a3832d3221b02fdec22eb08b21c693ddd

Observation ad9b67c9-ec2a-4b35-a13d-e96f8a5ba364 · outbound

This paper cites N., Tetzlaff, T., Nassar, M., Nikopour, H., and Talwar, S.

Percentile-Based Deep Reinforcement Learning and Reward Based Personalization For Delay Aware RAN Slicing in O-RAN N., Tetzlaff, T., Nassar, M., Nikopour, H., and Talwar, S

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:44:09.376636Z

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.

source=arxiv_source observed=2026-08-06T14:44:08.007335Z digest=sha256:f4c9a0b1cdb81a2ca3cee0da358d74a8e59aaf69b8d110e168da7bfcb3e90386

Observation faa741bd-6cbe-497d-96b2-abf7096f6429 · outbound

This paper cites Understanding o-ran: Architecture, interfaces, algorithms, security, and research challenges.

Percentile-Based Deep Reinforcement Learning and Reward Based Personalization For Delay Aware RAN Slicing in O-RAN Understanding o-ran: Architecture, interfaces, algorithms, security, and research challenges

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:44:09.360934Z

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.

source=arxiv_source observed=2026-08-06T14:44:08.122967Z digest=sha256:628946d6a6958f3743b4a3030691ea2bbf190435f78d6a68a714b700c5c1e241

Observation 639b0dd3-46b6-4a02-8e33-e6b91eca59d3 · outbound

This paper cites F., Simeone, O., and Durisi, G.

Percentile-Based Deep Reinforcement Learning and Reward Based Personalization For Delay Aware RAN Slicing in O-RAN F., Simeone, O., and Durisi, G

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:44:09.346711Z

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.

source=arxiv_source observed=2026-08-06T14:44:08.232619Z digest=sha256:bb831491959b490fb8b5935d843163ffef3b098c82aedbaa1020718b5fe83e9c

Observation 92f9d7af-04a6-4569-b7d3-4e80ab6a86c4 · outbound

This paper cites Queue-Learning: A Reinforcement Learning Approach for Providing Quality of Service.

Percentile-Based Deep Reinforcement Learning and Reward Based Personalization For Delay Aware RAN Slicing in O-RAN Queue-Learning: A Reinforcement Learning Approach for Providing Quality of Service

Reference 33

Resolution
verified exact
local_arxiv, observed 2026-08-06T14:44:09.146120Z

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.

source=arxiv_source observed=2026-08-06T14:44:08.311685Z digest=sha256:f8c2b8f7e9af1df627e5dcfbadf4a31aa89dc38a4dbe960a58c09109881943d1

Observation 59a404ba-160c-4648-91f6-6fc8c0961386 · outbound

This paper cites S., Demir, U., Stephenson, N., Soltani, N., Shah, V.

Percentile-Based Deep Reinforcement Learning and Reward Based Personalization For Delay Aware RAN Slicing in O-RAN S., Demir, U., Stephenson, N., Soltani, N., Shah, V

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:44:09.331669Z

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.

source=arxiv_source observed=2026-08-06T14:44:08.420724Z digest=sha256:4072076170cad243d7112c3f5c13a4581702b2c3b90780e6256bc80133f5e54e

Observation 04bf5e11-1bfd-49da-9709-d671049feb5d · outbound

This paper cites On the specialization of fdrl agents for scalable and distributed 6g ran slicing orchestration.

Percentile-Based Deep Reinforcement Learning and Reward Based Personalization For Delay Aware RAN Slicing in O-RAN On the specialization of fdrl agents for scalable and distributed 6g ran slicing orchestration

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:44:09.317706Z

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.

source=arxiv_source observed=2026-08-06T14:44:08.559917Z digest=sha256:3fb996d4e84f28a45db2dbcbe73d87c4b9486720fb97dbb2ff95bdded2634e9b

Observation 69de93e1-7ae0-4748-815e-b5b581caba40 · outbound

This paper cites an unresolved cited work.

Percentile-Based Deep Reinforcement Learning and Reward Based Personalization For Delay Aware RAN Slicing in O-RAN Unresolved cited work

Reference 36

Resolution
unresolved
raw_fallback, observed 2026-08-06T14:44:09.302173Z

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.

source=arxiv_source observed=2026-08-06T14:44:08.668201Z digest=sha256:6b14fe18fb69d95423143f13bc627a410a727adae39905c2a9985977bbc03ff3

Observation f0a0504d-f1b1-4a7c-817d-18fcd9ea69ef · outbound

This paper cites K., and Radunovic, B.

Percentile-Based Deep Reinforcement Learning and Reward Based Personalization For Delay Aware RAN Slicing in O-RAN K., and Radunovic, B

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:44:09.284715Z

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.

source=arxiv_source observed=2026-08-06T14:44:08.792988Z digest=sha256:5e66c6eae10ddc4c5c266bc2b9c89ff4af4200b12961efedfe5f1b3e446a42f9

Observation 8e89000d-6db3-402a-8d7e-f9c3288d5b9b · outbound

This paper cites Research report on digital twin ran use cases, May 2024 b.

Percentile-Based Deep Reinforcement Learning and Reward Based Personalization For Delay Aware RAN Slicing in O-RAN Research report on digital twin ran use cases, May 2024 b

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:44:09.269844Z

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.

source=arxiv_source observed=2026-08-06T14:44:08.877547Z digest=sha256:8450ac9415682448929d0d19e4d8c27bb6dacd472f5971a3ab2bbae62d14e613

Observation fdc0d8e7-cf29-41c2-a8a7-804c220fea40 · outbound

This paper cites Federated deep reinforcement learning for the distributed control of nextg wireless networks.

Percentile-Based Deep Reinforcement Learning and Reward Based Personalization For Delay Aware RAN Slicing in O-RAN Federated deep reinforcement learning for the distributed control of nextg wireless networks

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:44:09.250965Z

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.

source=arxiv_source observed=2026-08-06T14:44:08.997218Z digest=sha256:6d385bc6e2e533d768ce49d4f692fcf321c8098762cfa1e3a697e04c2e26bf96

Observation 0f1d2bd2-c7a6-4d92-854b-340e6a580114 · outbound

This paper cites Off-policy learning in contextual bandits for remote electrical tilt optimization.

Percentile-Based Deep Reinforcement Learning and Reward Based Personalization For Delay Aware RAN Slicing in O-RAN Off-policy learning in contextual bandits for remote electrical tilt optimization

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:44:09.235445Z

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.

source=arxiv_source observed=2026-08-06T14:44:09.044597Z digest=sha256:5bd32977366a85f083475fec3659a7cc797d70f2d3559e7624a9e5a1fda45805

Observation fa464990-ded6-4646-a556-eabb8db6b8a8 · outbound

This paper cites and Liyanage, M.

Percentile-Based Deep Reinforcement Learning and Reward Based Personalization For Delay Aware RAN Slicing in O-RAN and Liyanage, M

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:44:09.219338Z

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.

source=arxiv_source observed=2026-08-06T14:44:09.049533Z digest=sha256:fbbe48ffebbd9c7b296043fd7e2164983581ca0bad2b06ab25514ccc1a0a9b52

Observation 2410e9b3-0775-4ba1-b4f5-bc675b98f794 · outbound

This paper cites Dynamic ran slicing for service-oriented vehicular networks via constrained learning.

Percentile-Based Deep Reinforcement Learning and Reward Based Personalization For Delay Aware RAN Slicing in O-RAN Dynamic ran slicing for service-oriented vehicular networks via constrained learning

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:44:09.203303Z

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.

source=arxiv_source observed=2026-08-06T14:44:09.056196Z digest=sha256:fdef9bfd8df165f3cc0d79b233e447270d31c03471b13d5b73673d564f751a36

Observation 08361a61-e9cf-4c26-bfd9-72cd74450e8f · outbound

This paper cites Machine Learning-based Early Attack Detection Using Open RAN Intelligent Controller.

Percentile-Based Deep Reinforcement Learning and Reward Based Personalization For Delay Aware RAN Slicing in O-RAN Machine Learning-based Early Attack Detection Using Open RAN Intelligent Controller

Reference 43

Resolution
metadata mismatch
local_arxiv, observed 2026-08-06T14:44:09.121038Z

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.

source=arxiv_source observed=2026-08-06T14:44:09.063049Z digest=sha256:23fc6157441b01c33ad9e8d938003b562699fb7ce76f5fd036333c651e71d94c

Observation 9c9e9a99-299b-42c5-b1ec-c25682cfcce8 · outbound

This paper cites Q., Chen, J., Cao, X., and Wu, D.

Percentile-Based Deep Reinforcement Learning and Reward Based Personalization For Delay Aware RAN Slicing in O-RAN Q., Chen, J., Cao, X., and Wu, D

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:44:09.188356Z

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.

source=arxiv_source observed=2026-08-06T14:44:09.069227Z digest=sha256:ae9e6701b3b8dc61010c410cdf06fd60056759748e95b5fe8d4879478a823355

Pith citing papers

No inbound Pith citation observations are available.