{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:WDWOOBJ24THCH42CKRZHLOWPQB","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"3e0e56d2b6e5e41e3cada2d840b40b4ee487a4a7f891ac0b75ac45268a35dc36","cross_cats_sorted":["math.IT"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IT","submitted_at":"2022-01-25T12:49:25Z","title_canon_sha256":"6e0a2df612967134e28c0593dd234f8987a02e543f73355ec6ddda5fa41d961f"},"schema_version":"1.0","source":{"id":"2201.10281","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2201.10281","created_at":"2026-07-05T03:51:17Z"},{"alias_kind":"arxiv_version","alias_value":"2201.10281v1","created_at":"2026-07-05T03:51:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2201.10281","created_at":"2026-07-05T03:51:17Z"},{"alias_kind":"pith_short_12","alias_value":"WDWOOBJ24THC","created_at":"2026-07-05T03:51:17Z"},{"alias_kind":"pith_short_16","alias_value":"WDWOOBJ24THCH42C","created_at":"2026-07-05T03:51:17Z"},{"alias_kind":"pith_short_8","alias_value":"WDWOOBJ2","created_at":"2026-07-05T03:51:17Z"}],"graph_snapshots":[{"event_id":"sha256:1c6d3bbee958c826df81ad10250fb1a3e21ffca5dc244e2929c80de73b04be11","target":"graph","created_at":"2026-07-05T03:51:17Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2201.10281/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this paper, we propose a novel deep reinforcement learning framework to maximize user fairness in terms of delay. To this end, we devise a new version of the modified largest weighted delay first (M-LWDF) algorithm, which is called $\\beta$-M-LWDF, aiming to fulfill an appropriate balance between user fairness and average delay. This balance is defined as a feasible region on the cumulative distribution function (CDF) of the user delay that allows identifying unfair states, feasible-fair states, and over-fair states. Simulation results reveal that our proposed framework outperforms tradition","authors_text":"A. Villena-Rodr\\'iguez, F. J. Mart\\'in-Vega, G. G\\'omez, M. C. Aguayo-Torres, M. L\\'opez-S\\'anchez","cross_cats":["math.IT"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IT","submitted_at":"2022-01-25T12:49:25Z","title":"Latency Fairness Optimization on Wireless Networks through Deep Reinforcement Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2201.10281","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:e74ec09fe61e512d2fe43683b980243aedf3f17e70ab6d9df7dd655b0f52a439","target":"record","created_at":"2026-07-05T03:51:17Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"3e0e56d2b6e5e41e3cada2d840b40b4ee487a4a7f891ac0b75ac45268a35dc36","cross_cats_sorted":["math.IT"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IT","submitted_at":"2022-01-25T12:49:25Z","title_canon_sha256":"6e0a2df612967134e28c0593dd234f8987a02e543f73355ec6ddda5fa41d961f"},"schema_version":"1.0","source":{"id":"2201.10281","kind":"arxiv","version":1}},"canonical_sha256":"b0ece7053ae4ce23f342547275bacf80577875d20ecbb37550813c02e9029a48","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b0ece7053ae4ce23f342547275bacf80577875d20ecbb37550813c02e9029a48","first_computed_at":"2026-07-05T03:51:17.558088Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:51:17.558088Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"owCAn3KQXBVr9t3CldzwY9FOq+duqMoo+PtDGWRLL6g5xtFSlfiPMlFB8QG42lCb6OHekQKodaH0ECOOynd+AA==","signature_status":"signed_v1","signed_at":"2026-07-05T03:51:17.558467Z","signed_message":"canonical_sha256_bytes"},"source_id":"2201.10281","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e74ec09fe61e512d2fe43683b980243aedf3f17e70ab6d9df7dd655b0f52a439","sha256:1c6d3bbee958c826df81ad10250fb1a3e21ffca5dc244e2929c80de73b04be11"],"state_sha256":"888ca52739ec2b002f6949a9e0fc9813189447a887555be05870c6fa14f27310"}