{"as_of":"2026-08-22T02:04:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7aa021764d81f834daf8810c85aa77151ef878910f5f6aef347cd7b8c17fa6f5","coverage":[{"denominator":17,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":17,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T11:44:58.151295Z","state":"measured"},{"denominator":17,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":17,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-21T06:32:19.484+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2504.14749/citation-record","integrity":"/paper/2504.14749/integrity","json":"/paper/2504.14749/citation-record.json","paper":"/paper/2504.14749"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:44:58.447564Z","title":"Energy efficiency enhancements in radio access networks,","venue":null,"work_id":"47931f0d-fa2b-45f7-85b5-f9bf84051fb6","year":2004},"citing_paper":{"arxiv_id":"2504.14749","last_updated":"2025-04-20T21:42:25Z","snapshot_observed_at":"2026-08-17T20:04:06.182779Z","submitted_at":"2025-04-20T21:42:25Z","title":"PPO-EPO: Energy and Performance Optimization for O-RAN Using Reinforcement Learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-16T11:44:58.070142Z"},"links":{"citing_paper":"/paper/2504.14749"},"observation_digest":"sha256:40b0cbebb183752285ec962d48fe5e8bf68e1e26a2646116cf37fd92644b008b","observation_id":"448850e6-492c-4754-83ba-c14ed9259f00","resolution":{"observed_at":"2026-08-16T11:44:58.452721Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:44:58.430237Z","title":"Energy-and spectral-efficiency trade-off in ofdma-based cooperative cognitive radio networks,","venue":null,"work_id":"a277f44c-0778-44e5-9b9c-d02415e56c43","year":2014},"citing_paper":{"arxiv_id":"2504.14749","last_updated":"2025-04-20T21:42:25Z","snapshot_observed_at":"2026-08-17T20:04:06.182779Z","submitted_at":"2025-04-20T21:42:25Z","title":"PPO-EPO: Energy and Performance Optimization for O-RAN Using Reinforcement Learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-16T11:44:58.075456Z"},"links":{"citing_paper":"/paper/2504.14749"},"observation_digest":"sha256:beb029b62ed08d781ba81b0331d77222126e0197ec7f5cfd83f7b11056595506","observation_id":"c5e74190-9ab4-45cd-b545-c2b56a3d6515","resolution":{"observed_at":"2026-08-16T11:44:58.436497Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:44:58.413291Z","title":"A survey of energy-efficient wireless communications,","venue":null,"work_id":"986ac210-c154-479c-99cc-d0a824dfda45","year":2013},"citing_paper":{"arxiv_id":"2504.14749","last_updated":"2025-04-20T21:42:25Z","snapshot_observed_at":"2026-08-17T20:04:06.182779Z","submitted_at":"2025-04-20T21:42:25Z","title":"PPO-EPO: Energy and Performance Optimization for O-RAN Using Reinforcement Learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-16T11:44:58.080461Z"},"links":{"citing_paper":"/paper/2504.14749"},"observation_digest":"sha256:fd347691c114094b8890925b104a8f158aae4d64e9db1c1baaf1821df3b3ea1a","observation_id":"b729f501-f305-4910-befe-cd50e21cf8aa","resolution":{"observed_at":"2026-08-16T11:44:58.418926Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:44:58.395328Z","title":"Holistic approach for future energy effi- cient cellular networks,","venue":null,"work_id":"2031d080-d9d7-4122-9db4-65f52d05871c","year":2010},"citing_paper":{"arxiv_id":"2504.14749","last_updated":"2025-04-20T21:42:25Z","snapshot_observed_at":"2026-08-17T20:04:06.182779Z","submitted_at":"2025-04-20T21:42:25Z","title":"PPO-EPO: Energy and Performance Optimization for O-RAN Using Reinforcement Learning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-16T11:44:58.085422Z"},"links":{"citing_paper":"/paper/2504.14749"},"observation_digest":"sha256:1b20fb3b28a3b0241bcb23f41e070879817e0cc51a681d288f2b02364cd94778","observation_id":"565a0eec-cdc6-4b5d-ab7a-c8da350afdfd","resolution":{"observed_at":"2026-08-16T11:44:58.401332Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.11993","last_updated":"2024-02-19T09:38:55Z","snapshot_observed_at":"2026-08-16T14:17:24.263879Z","submitted_at":"2024-02-19T09:38:55Z","title":"Towards Energy Efficient RAN: From Industry Standards to Trending Practice","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.11993","snapshot_observed_at":"2026-08-16T11:44:58.090161Z","title":"Towards energy efficient ran: From industry standards to trending practice,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.14749","last_updated":"2025-04-20T21:42:25Z","snapshot_observed_at":"2026-08-17T20:04:06.182779Z","submitted_at":"2025-04-20T21:42:25Z","title":"PPO-EPO: Energy and Performance Optimization for O-RAN Using Reinforcement Learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-16T11:44:58.090161Z"},"links":{"cited_paper":"/paper/2402.11993","citing_paper":"/paper/2504.14749"},"observation_digest":"sha256:301b73125b0e7a2495636c6f57b215051df64d5ee4c86ae16a430970584667bc","observation_id":"869a7cdd-addb-4d03-942e-63fa76c03900","resolution":{"observed_at":"2026-08-16T11:44:58.090161Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.15098","last_updated":"2024-09-23T15:11:32Z","snapshot_observed_at":"2026-08-21T04:04:34.282972Z","submitted_at":"2024-09-23T15:11:32Z","title":"Energy Saving in 6G O-RAN Using DQN-based xApp","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.15098","snapshot_observed_at":"2026-08-16T11:44:58.096050Z","title":"Energy saving in 6g o-ran using dqn-based xapp,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.14749","last_updated":"2025-04-20T21:42:25Z","snapshot_observed_at":"2026-08-17T20:04:06.182779Z","submitted_at":"2025-04-20T21:42:25Z","title":"PPO-EPO: Energy and Performance Optimization for O-RAN Using Reinforcement Learning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-16T11:44:58.096050Z"},"links":{"cited_paper":"/paper/2409.15098","citing_paper":"/paper/2504.14749"},"observation_digest":"sha256:882b1b853ad92ba38146053afb6b59432c0c0059e0d51fa94cb4c8dd9b9b8a6c","observation_id":"20fffaad-34d6-471b-a597-c717ce84db01","resolution":{"observed_at":"2026-08-16T11:44:58.096050Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:44:58.101821Z","title":"Achieving energy efficiency in open radio access networks (oran) using xapps,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.14749","last_updated":"2025-04-20T21:42:25Z","snapshot_observed_at":"2026-08-17T20:04:06.182779Z","submitted_at":"2025-04-20T21:42:25Z","title":"PPO-EPO: Energy and Performance Optimization for O-RAN Using Reinforcement Learning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-16T11:44:58.101821Z"},"links":{"citing_paper":"/paper/2504.14749"},"observation_digest":"sha256:79d7a4e0f00704823892e42152ca54b8408e94304c2cb939963e386a442b62aa","observation_id":"7b4c8943-6753-40a6-959c-5fd75dbf91a0","resolution":{"observed_at":"2026-08-16T11:44:58.101821Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:44:58.368297Z","title":"Energy efficiency in wireless: Ran and beyond,","venue":null,"work_id":"707a5f72-5a9d-402c-b65c-089fe1d1e7ad","year":2023},"citing_paper":{"arxiv_id":"2504.14749","last_updated":"2025-04-20T21:42:25Z","snapshot_observed_at":"2026-08-17T20:04:06.182779Z","submitted_at":"2025-04-20T21:42:25Z","title":"PPO-EPO: Energy and Performance Optimization for O-RAN Using Reinforcement Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-16T11:44:58.106667Z"},"links":{"citing_paper":"/paper/2504.14749"},"observation_digest":"sha256:b9c86ee3fdeacd528ff2acf0a1dcd1485c4b18bb7cc609a4e49739db4b12b2b0","observation_id":"d7311d1d-a4b0-4ec7-bbcd-b85669fa4fd1","resolution":{"observed_at":"2026-08-16T11:44:58.373547Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:44:58.111271Z","title":"A survey of recent advances in optimization methods for wireless communications,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.14749","last_updated":"2025-04-20T21:42:25Z","snapshot_observed_at":"2026-08-17T20:04:06.182779Z","submitted_at":"2025-04-20T21:42:25Z","title":"PPO-EPO: Energy and Performance Optimization for O-RAN Using Reinforcement Learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-16T11:44:58.111271Z"},"links":{"citing_paper":"/paper/2504.14749"},"observation_digest":"sha256:ff4fc6c61bb95b3129cafdb3306dbf48abbc3d7cbd57fb7e7f0f7ac576e87a5a","observation_id":"9b7e9661-e45f-4f72-a839-f67938e37e09","resolution":{"observed_at":"2026-08-16T11:44:58.111271Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:44:58.339453Z","title":null,"venue":null,"work_id":"72662a6b-f654-4481-8a49-98abad913d7d","year":2024},"citing_paper":{"arxiv_id":"2504.14749","last_updated":"2025-04-20T21:42:25Z","snapshot_observed_at":"2026-08-17T20:04:06.182779Z","submitted_at":"2025-04-20T21:42:25Z","title":"PPO-EPO: Energy and Performance Optimization for O-RAN Using Reinforcement Learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-16T11:44:58.116210Z"},"links":{"citing_paper":"/paper/2504.14749"},"observation_digest":"sha256:f2188ee7912bfa2a368d5c1715f77b9db5ed7f8ca56b38b7027345782c42e06b","observation_id":"a9c23578-0c48-4f55-bbc5-9cf40b8ed3ee","resolution":{"observed_at":"2026-08-16T11:44:58.344079Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:44:58.322782Z","title":"Cell-free massive mimo in o-ran: Energy-aware joint orchestration of cloud, fronthaul, and radio resources,","venue":null,"work_id":"1c542091-473c-4ad5-b1ae-acda7298912c","year":2024},"citing_paper":{"arxiv_id":"2504.14749","last_updated":"2025-04-20T21:42:25Z","snapshot_observed_at":"2026-08-17T20:04:06.182779Z","submitted_at":"2025-04-20T21:42:25Z","title":"PPO-EPO: Energy and Performance Optimization for O-RAN Using Reinforcement Learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-16T11:44:58.121162Z"},"links":{"citing_paper":"/paper/2504.14749"},"observation_digest":"sha256:33adf8289b02584866111aeb87a4ef02c4049f365356ddd95dfd844b6fbea383","observation_id":"314ae9fd-e7d7-4af9-a849-b7659638fbc5","resolution":{"observed_at":"2026-08-16T11:44:58.328575Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.14021","last_updated":"2024-10-17T20:48:37Z","snapshot_observed_at":"2026-08-16T13:08:14.329237Z","submitted_at":"2024-10-17T20:48:37Z","title":"Design and Evaluation of Deep Reinforcement Learning for Energy Saving in Open RAN","version":1},"cited_work":{"arxiv_id":"2410.14021","doi":null,"metadata_source":"pith","pith_arxiv_id":"2410.14021","snapshot_observed_at":"2026-08-16T11:44:58.213187Z","title":"Design and Evaluation of Deep Reinforcement Learning for Energy Saving in Open RAN","venue":"cs.NI","work_id":"d5105795-3ac7-4138-b8c6-451a8fe0f98c","year":2024},"citing_paper":{"arxiv_id":"2504.14749","last_updated":"2025-04-20T21:42:25Z","snapshot_observed_at":"2026-08-17T20:04:06.182779Z","submitted_at":"2025-04-20T21:42:25Z","title":"PPO-EPO: Energy and Performance Optimization for O-RAN Using Reinforcement Learning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-16T11:44:58.125685Z"},"links":{"cited_paper":"/paper/2410.14021","citing_paper":"/paper/2504.14749"},"observation_digest":"sha256:ce4f75fba3db9b0364a901de0b4048df9a2b49e329569d00f06aa2be64bae80f","observation_id":"fbc70e92-f2e8-4069-8676-ae2f203def7f","resolution":{"observed_at":"2026-08-16T11:44:58.219041Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.10116","last_updated":"2024-05-16T14:09:43Z","snapshot_observed_at":"2026-08-16T13:52:09.309303Z","submitted_at":"2024-05-16T14:09:43Z","title":"Enhancing Energy Efficiency in O-RAN Through Intelligent xApps Deployment","version":1},"cited_work":{"arxiv_id":"2405.10116","doi":null,"metadata_source":"pith","pith_arxiv_id":"2405.10116","snapshot_observed_at":"2026-08-16T11:44:58.189197Z","title":"Enhancing Energy Efficiency in O-RAN Through Intelligent xApps Deployment","venue":"eess.SY","work_id":"3464102a-25fa-4c04-9867-3a6c0886e60e","year":2024},"citing_paper":{"arxiv_id":"2504.14749","last_updated":"2025-04-20T21:42:25Z","snapshot_observed_at":"2026-08-17T20:04:06.182779Z","submitted_at":"2025-04-20T21:42:25Z","title":"PPO-EPO: Energy and Performance Optimization for O-RAN Using Reinforcement Learning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-16T11:44:58.131026Z"},"links":{"cited_paper":"/paper/2405.10116","citing_paper":"/paper/2504.14749"},"observation_digest":"sha256:f2fa8dafd306a502fa067c71f958572b085704eca34a7257410ab9feb67e6664","observation_id":"996c888c-4caf-4094-aae2-55b92ffed985","resolution":{"observed_at":"2026-08-16T11:44:58.196102Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:44:58.306725Z","title":"Landscape-enabled al- gorithmic design for the cell switch-off problem in 5g ultra-dense networks,","venue":null,"work_id":"01052ac4-bd32-4d62-ad0c-a16b3fa2992e","year":2024},"citing_paper":{"arxiv_id":"2504.14749","last_updated":"2025-04-20T21:42:25Z","snapshot_observed_at":"2026-08-17T20:04:06.182779Z","submitted_at":"2025-04-20T21:42:25Z","title":"PPO-EPO: Energy and Performance Optimization for O-RAN Using Reinforcement Learning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-16T11:44:58.136241Z"},"links":{"citing_paper":"/paper/2504.14749"},"observation_digest":"sha256:2b5e051814c7ea73c82831420969fece8f52b6e21a2a557c9a981ce65f22a1a9","observation_id":"06af7853-9547-4fe1-9aab-dffd614f20ec","resolution":{"observed_at":"2026-08-16T11:44:58.312225Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:44:58.291084Z","title":"5g; study on channel model for frequencies from 0.5 to 100 ghz (3gpp tr 38.901 version 15.0.0 release 15),","venue":null,"work_id":"51524004-83a3-4464-8d9a-9c413af03624","year":2018},"citing_paper":{"arxiv_id":"2504.14749","last_updated":"2025-04-20T21:42:25Z","snapshot_observed_at":"2026-08-17T20:04:06.182779Z","submitted_at":"2025-04-20T21:42:25Z","title":"PPO-EPO: Energy and Performance Optimization for O-RAN Using Reinforcement Learning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-16T11:44:58.141157Z"},"links":{"citing_paper":"/paper/2504.14749"},"observation_digest":"sha256:a48c4ae9630235b652a5b971e6212d7d9415d57690c85d37ff9fd4a91c3336d5","observation_id":"1912224e-f4b2-4b02-b1a1-2fea6a247a6c","resolution":{"observed_at":"2026-08-16T11:44:58.296033Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:44:58.275316Z","title":"3rd generation partnership project (3gpp), “nr; radio resource control (rrc) protocol specification (release 18),","venue":null,"work_id":"fab170e5-b2f2-4f1b-9d26-48558001eb25","year":2023},"citing_paper":{"arxiv_id":"2504.14749","last_updated":"2025-04-20T21:42:25Z","snapshot_observed_at":"2026-08-17T20:04:06.182779Z","submitted_at":"2025-04-20T21:42:25Z","title":"PPO-EPO: Energy and Performance Optimization for O-RAN Using Reinforcement Learning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-16T11:44:58.146189Z"},"links":{"citing_paper":"/paper/2504.14749"},"observation_digest":"sha256:7fbdd5b44d2945a156eb58a0573ac78aaf6c00387ca7e09d0b6b67afeb285ca3","observation_id":"428c0abc-474b-4200-93fe-1b3281da51f0","resolution":{"observed_at":"2026-08-16T11:44:58.280363Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:44:58.151295Z","title":"Stable-baselines3: Reliable reinforcement learning implementa- tions,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2504.14749","last_updated":"2025-04-20T21:42:25Z","snapshot_observed_at":"2026-08-17T20:04:06.182779Z","submitted_at":"2025-04-20T21:42:25Z","title":"PPO-EPO: Energy and Performance Optimization for O-RAN Using Reinforcement Learning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-16T11:44:58.151295Z"},"links":{"citing_paper":"/paper/2504.14749"},"observation_digest":"sha256:3460a164db13fb4cfce434c5db36077f8e9a97d99bcae6eeaf976f128e6d7516","observation_id":"723179bc-ad8d-4a25-8877-24274a2d00ad","resolution":{"observed_at":"2026-08-16T11:44:58.151295Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2504.14749","last_updated":"2025-04-20T21:42:25Z","latest_version":1,"primary_category":"cs.NI","snapshot_observed_at":"2026-08-17T20:04:06.182779Z","submitted_at":"2025-04-20T21:42:25Z","title":"PPO-EPO: Energy and Performance Optimization for O-RAN Using Reinforcement Learning"},"reference_resolution":{"displayed":17,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":6,"verified_exact":2,"verified_fuzzy":9},"total_outbound_references":17},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 22 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 0 inbound Pith citation observations for arXiv:2504.14749."}