{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:45CX5VXBX626C5SEA2R3P3ZHAG","short_pith_number":"pith:45CX5VXB","schema_version":"1.0","canonical_sha256":"e7457ed6e1bfb5e1764406a3b7ef27019163063cf91728c806ff36e40459a757","source":{"kind":"arxiv","id":"2606.30031","version":1},"attestation_state":"computed","paper":{"title":"Joint Outage Detection and Compensation for Self-Healing 5G RAN via Deep Reinforcement Learning","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Sajjad Hussain","submitted_at":"2026-06-29T09:30:54Z","abstract_excerpt":"Self-healing radio access network (RAN) requires autonomous detection and compensation of base station (BS) failures. This letter proposes an end-to-end framework combining three-class cell outage detection (COD), distinguishing normal, failed, and collaterally degraded cells, with a deep Q-Network (DQN) based deep reinforcement learning (DRL) agent that jointly controls power and antenna tilt for cell outage compensation (COC). Evaluation results show that the proposed DQN agent achieves 99.1% coverage and 54% full-recovery rate, an 11$\\times$ improvement over the best heuristic, while consum"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2606.30031","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"eess.SP","submitted_at":"2026-06-29T09:30:54Z","cross_cats_sorted":[],"title_canon_sha256":"8adb4b590a06f212674fd0fadc722858e520177df4765fb88963376383dc696a","abstract_canon_sha256":"8cc666a28f43a4badaa0ea6a0c7f027cbaec3893253e5ddd4290ceb2cc1541c2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-30T02:17:46.886013Z","signature_b64":"XtVIgposZDtYSVAtZiUoDPtD9lH0+KQHr+tbpH5oHuMDQVyShcnHOeDZdcyLZCAXoZpPdiT2cdPe85mLccx1BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e7457ed6e1bfb5e1764406a3b7ef27019163063cf91728c806ff36e40459a757","last_reissued_at":"2026-06-30T02:17:46.885293Z","signature_status":"signed_v1","first_computed_at":"2026-06-30T02:17:46.885293Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Joint Outage Detection and Compensation for Self-Healing 5G RAN via Deep Reinforcement Learning","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Sajjad Hussain","submitted_at":"2026-06-29T09:30:54Z","abstract_excerpt":"Self-healing radio access network (RAN) requires autonomous detection and compensation of base station (BS) failures. This letter proposes an end-to-end framework combining three-class cell outage detection (COD), distinguishing normal, failed, and collaterally degraded cells, with a deep Q-Network (DQN) based deep reinforcement learning (DRL) agent that jointly controls power and antenna tilt for cell outage compensation (COC). Evaluation results show that the proposed DQN agent achieves 99.1% coverage and 54% full-recovery rate, an 11$\\times$ improvement over the best heuristic, while consum"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.30031","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2606.30031/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2606.30031","created_at":"2026-06-30T02:17:46.885405+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.30031v1","created_at":"2026-06-30T02:17:46.885405+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.30031","created_at":"2026-06-30T02:17:46.885405+00:00"},{"alias_kind":"pith_short_12","alias_value":"45CX5VXBX626","created_at":"2026-06-30T02:17:46.885405+00:00"},{"alias_kind":"pith_short_16","alias_value":"45CX5VXBX626C5SE","created_at":"2026-06-30T02:17:46.885405+00:00"},{"alias_kind":"pith_short_8","alias_value":"45CX5VXB","created_at":"2026-06-30T02:17:46.885405+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/45CX5VXBX626C5SEA2R3P3ZHAG","json":"https://pith.science/pith/45CX5VXBX626C5SEA2R3P3ZHAG.json","graph_json":"https://pith.science/api/pith-number/45CX5VXBX626C5SEA2R3P3ZHAG/graph.json","events_json":"https://pith.science/api/pith-number/45CX5VXBX626C5SEA2R3P3ZHAG/events.json","paper":"https://pith.science/paper/45CX5VXB"},"agent_actions":{"view_html":"https://pith.science/pith/45CX5VXBX626C5SEA2R3P3ZHAG","download_json":"https://pith.science/pith/45CX5VXBX626C5SEA2R3P3ZHAG.json","view_paper":"https://pith.science/paper/45CX5VXB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.30031&json=true","fetch_graph":"https://pith.science/api/pith-number/45CX5VXBX626C5SEA2R3P3ZHAG/graph.json","fetch_events":"https://pith.science/api/pith-number/45CX5VXBX626C5SEA2R3P3ZHAG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/45CX5VXBX626C5SEA2R3P3ZHAG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/45CX5VXBX626C5SEA2R3P3ZHAG/action/storage_attestation","attest_author":"https://pith.science/pith/45CX5VXBX626C5SEA2R3P3ZHAG/action/author_attestation","sign_citation":"https://pith.science/pith/45CX5VXBX626C5SEA2R3P3ZHAG/action/citation_signature","submit_replication":"https://pith.science/pith/45CX5VXBX626C5SEA2R3P3ZHAG/action/replication_record"}},"created_at":"2026-06-30T02:17:46.885405+00:00","updated_at":"2026-06-30T02:17:46.885405+00:00"}