{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:HV37WFL7D4VRVU3TKW5QKY74E4","short_pith_number":"pith:HV37WFL7","canonical_record":{"source":{"id":"2605.00457","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NI","submitted_at":"2026-05-01T06:43:09Z","cross_cats_sorted":["cs.LG","cs.SY","eess.SY"],"title_canon_sha256":"5ab028d0820d5fda486924dfb0e4ff4f873c29200ed1bd7747776eb36f79a868","abstract_canon_sha256":"14187a97355e550c5fc8b686fade400d1174df56fd08762bb0a75fc4e3aa21db"},"schema_version":"1.0"},"canonical_sha256":"3d77fb157f1f2b1ad37355bb0563fc272966517657e12b071ad35f4dc744da4a","source":{"kind":"arxiv","id":"2605.00457","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.00457","created_at":"2026-05-28T01:04:41Z"},{"alias_kind":"arxiv_version","alias_value":"2605.00457v3","created_at":"2026-05-28T01:04:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.00457","created_at":"2026-05-28T01:04:41Z"},{"alias_kind":"pith_short_12","alias_value":"HV37WFL7D4VR","created_at":"2026-05-28T01:04:41Z"},{"alias_kind":"pith_short_16","alias_value":"HV37WFL7D4VRVU3T","created_at":"2026-05-28T01:04:41Z"},{"alias_kind":"pith_short_8","alias_value":"HV37WFL7","created_at":"2026-05-28T01:04:41Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:HV37WFL7D4VRVU3TKW5QKY74E4","target":"record","payload":{"canonical_record":{"source":{"id":"2605.00457","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NI","submitted_at":"2026-05-01T06:43:09Z","cross_cats_sorted":["cs.LG","cs.SY","eess.SY"],"title_canon_sha256":"5ab028d0820d5fda486924dfb0e4ff4f873c29200ed1bd7747776eb36f79a868","abstract_canon_sha256":"14187a97355e550c5fc8b686fade400d1174df56fd08762bb0a75fc4e3aa21db"},"schema_version":"1.0"},"canonical_sha256":"3d77fb157f1f2b1ad37355bb0563fc272966517657e12b071ad35f4dc744da4a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-28T01:04:41.258116Z","signature_b64":"ns1hRP59UaiY7GgbDJuHNU78Irgp32DAPuHxYIC/wM+egVHBr6Jb/Kx7EeiJ9ZBVmv0q7E6yVzw/zbiW0T+2CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3d77fb157f1f2b1ad37355bb0563fc272966517657e12b071ad35f4dc744da4a","last_reissued_at":"2026-05-28T01:04:41.257608Z","signature_status":"signed_v1","first_computed_at":"2026-05-28T01:04:41.257608Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.00457","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-28T01:04:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9XSj/F9nEFaMXekY3XwVUpS9UgaAbnpcRL8JVtVbUGM3a0/xpL5cb7Q4loFuxqxFmxmVUu098H2NtfDQ4piNBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T07:15:41.404537Z"},"content_sha256":"6b4f9db2baaad45783983c80e73fb8ae10a87448aec1a59e49512bd0a4cdc9f8","schema_version":"1.0","event_id":"sha256:6b4f9db2baaad45783983c80e73fb8ae10a87448aec1a59e49512bd0a4cdc9f8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:HV37WFL7D4VRVU3TKW5QKY74E4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Policy-Driven DRL-Based TXOP Adaptation in NR-U and Wi-Fi Coexistence","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"A policy-driven deep reinforcement learning framework models NR-U and Wi-Fi coexistence as a Markov decision process and uses reward design to learn TXOP control policies that set explicit operating points on the fairness-throughput curve.","cross_cats":["cs.LG","cs.SY","eess.SY"],"primary_cat":"cs.NI","authors_text":"Chiapin Wang, Po-Heng Chou, Shou-Yu Chen, Yi-Fang Yu","submitted_at":"2026-05-01T06:43:09Z","abstract_excerpt":"The coexistence of NR-U and Wi-Fi in unlicensed spectrum introduces a challenging coexistence management problem, where heterogeneous channel access mechanisms lead to a significant imbalance in spectrum utilization and degraded Wi-Fi performance. To address this challenge, we propose a policy-driven deep reinforcement learning (DRL) framework for adaptive transmission opportunity (TXOP) control, in which the coexistence process is formulated as a Markov decision process (MDP) and a deep Q-network (DQN) learns control policies through online interaction. A key contribution is the introduction "},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Simulation results show that the proposed framework achieves a Jain fairness index above 0.9 under strict fairness control. Compared to absolute fairness, moderate fairness improves aggregate throughput by 68.22%, while the utility-based policy further enhances utility by 177.6%.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The coexistence dynamics can be faithfully captured as a Markov decision process whose state and reward signals allow a DQN to learn stable policies that generalize beyond the simulated scenarios.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A DRL framework with tunable reward policies controls TXOP in NR-U/Wi-Fi coexistence to achieve Jain fairness above 0.9 and throughput/utility gains of 68-177% in simulations.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A policy-driven deep reinforcement learning framework models NR-U and Wi-Fi coexistence as a Markov decision process and uses reward design to learn TXOP control policies that set explicit operating points on the fairness-throughput curve.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"61f81ce2fc691f971937de94da233ae318e51e273f47305bc7fc4f57deb36a6e"},"source":{"id":"2605.00457","kind":"arxiv","version":3},"verdict":{"id":"a4530fe5-9d59-410a-baf6-49cb4648f376","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-09T19:00:02.200119Z","strongest_claim":"Simulation results show that the proposed framework achieves a Jain fairness index above 0.9 under strict fairness control. Compared to absolute fairness, moderate fairness improves aggregate throughput by 68.22%, while the utility-based policy further enhances utility by 177.6%.","one_line_summary":"A DRL framework with tunable reward policies controls TXOP in NR-U/Wi-Fi coexistence to achieve Jain fairness above 0.9 and throughput/utility gains of 68-177% in simulations.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The coexistence dynamics can be faithfully captured as a Markov decision process whose state and reward signals allow a DQN to learn stable policies that generalize beyond the simulated scenarios.","pith_extraction_headline":"A policy-driven deep reinforcement learning framework models NR-U and Wi-Fi coexistence as a Markov decision process and uses reward design to learn TXOP control policies that set explicit operating points on the fairness-throughput curve."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.00457/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"ai_meta_artifact","ran_at":"2026-05-20T19:41:44.267011Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T18:07:27.165103Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"f550ce65b13dafbf29fe15693d7c88f410410b6578cdeec0b3b67ffbe436e3f9"},"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"},"verdict_id":"a4530fe5-9d59-410a-baf6-49cb4648f376"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-28T01:04:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FpTutq5+lLZEuizSGejV6o8cy8FB5/0WA9AbIo8jbiKjYSHwufhgBCE74H0c7pKrWbcba2hF85A8MpvEFPmaCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T07:15:41.405326Z"},"content_sha256":"dfa43e8d405b47cf2350a258c1cce580ad2a4512f028c542e4a2711aeb259198","schema_version":"1.0","event_id":"sha256:dfa43e8d405b47cf2350a258c1cce580ad2a4512f028c542e4a2711aeb259198"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HV37WFL7D4VRVU3TKW5QKY74E4/bundle.json","state_url":"https://pith.science/pith/HV37WFL7D4VRVU3TKW5QKY74E4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HV37WFL7D4VRVU3TKW5QKY74E4/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-07T07:15:41Z","links":{"resolver":"https://pith.science/pith/HV37WFL7D4VRVU3TKW5QKY74E4","bundle":"https://pith.science/pith/HV37WFL7D4VRVU3TKW5QKY74E4/bundle.json","state":"https://pith.science/pith/HV37WFL7D4VRVU3TKW5QKY74E4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HV37WFL7D4VRVU3TKW5QKY74E4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:HV37WFL7D4VRVU3TKW5QKY74E4","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":"14187a97355e550c5fc8b686fade400d1174df56fd08762bb0a75fc4e3aa21db","cross_cats_sorted":["cs.LG","cs.SY","eess.SY"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NI","submitted_at":"2026-05-01T06:43:09Z","title_canon_sha256":"5ab028d0820d5fda486924dfb0e4ff4f873c29200ed1bd7747776eb36f79a868"},"schema_version":"1.0","source":{"id":"2605.00457","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.00457","created_at":"2026-05-28T01:04:41Z"},{"alias_kind":"arxiv_version","alias_value":"2605.00457v3","created_at":"2026-05-28T01:04:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.00457","created_at":"2026-05-28T01:04:41Z"},{"alias_kind":"pith_short_12","alias_value":"HV37WFL7D4VR","created_at":"2026-05-28T01:04:41Z"},{"alias_kind":"pith_short_16","alias_value":"HV37WFL7D4VRVU3T","created_at":"2026-05-28T01:04:41Z"},{"alias_kind":"pith_short_8","alias_value":"HV37WFL7","created_at":"2026-05-28T01:04:41Z"}],"graph_snapshots":[{"event_id":"sha256:dfa43e8d405b47cf2350a258c1cce580ad2a4512f028c542e4a2711aeb259198","target":"graph","created_at":"2026-05-28T01:04:41Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"Simulation results show that the proposed framework achieves a Jain fairness index above 0.9 under strict fairness control. Compared to absolute fairness, moderate fairness improves aggregate throughput by 68.22%, while the utility-based policy further enhances utility by 177.6%."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The coexistence dynamics can be faithfully captured as a Markov decision process whose state and reward signals allow a DQN to learn stable policies that generalize beyond the simulated scenarios."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"A DRL framework with tunable reward policies controls TXOP in NR-U/Wi-Fi coexistence to achieve Jain fairness above 0.9 and throughput/utility gains of 68-177% in simulations."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"A policy-driven deep reinforcement learning framework models NR-U and Wi-Fi coexistence as a Markov decision process and uses reward design to learn TXOP control policies that set explicit operating points on the fairness-throughput curve."}],"snapshot_sha256":"61f81ce2fc691f971937de94da233ae318e51e273f47305bc7fc4f57deb36a6e"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-20T19:41:44.267011Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"doi_compliance","ran_at":"2026-05-19T18:07:27.165103Z","status":"completed","version":"1.0.0"}],"endpoint":"/pith/2605.00457/integrity.json","findings":[],"snapshot_sha256":"f550ce65b13dafbf29fe15693d7c88f410410b6578cdeec0b3b67ffbe436e3f9","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The coexistence of NR-U and Wi-Fi in unlicensed spectrum introduces a challenging coexistence management problem, where heterogeneous channel access mechanisms lead to a significant imbalance in spectrum utilization and degraded Wi-Fi performance. To address this challenge, we propose a policy-driven deep reinforcement learning (DRL) framework for adaptive transmission opportunity (TXOP) control, in which the coexistence process is formulated as a Markov decision process (MDP) and a deep Q-network (DQN) learns control policies through online interaction. A key contribution is the introduction ","authors_text":"Chiapin Wang, Po-Heng Chou, Shou-Yu Chen, Yi-Fang Yu","cross_cats":["cs.LG","cs.SY","eess.SY"],"headline":"A policy-driven deep reinforcement learning framework models NR-U and Wi-Fi coexistence as a Markov decision process and uses reward design to learn TXOP control policies that set explicit operating points on the fairness-throughput curve.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NI","submitted_at":"2026-05-01T06:43:09Z","title":"Policy-Driven DRL-Based TXOP Adaptation in NR-U and Wi-Fi Coexistence"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.00457","kind":"arxiv","version":3},"verdict":{"created_at":"2026-05-09T19:00:02.200119Z","id":"a4530fe5-9d59-410a-baf6-49cb4648f376","model_set":{"reader":"grok-4.3"},"one_line_summary":"A DRL framework with tunable reward policies controls TXOP in NR-U/Wi-Fi coexistence to achieve Jain fairness above 0.9 and throughput/utility gains of 68-177% in simulations.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"A policy-driven deep reinforcement learning framework models NR-U and Wi-Fi coexistence as a Markov decision process and uses reward design to learn TXOP control policies that set explicit operating points on the fairness-throughput curve.","strongest_claim":"Simulation results show that the proposed framework achieves a Jain fairness index above 0.9 under strict fairness control. Compared to absolute fairness, moderate fairness improves aggregate throughput by 68.22%, while the utility-based policy further enhances utility by 177.6%.","weakest_assumption":"The coexistence dynamics can be faithfully captured as a Markov decision process whose state and reward signals allow a DQN to learn stable policies that generalize beyond the simulated scenarios."}},"verdict_id":"a4530fe5-9d59-410a-baf6-49cb4648f376"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:6b4f9db2baaad45783983c80e73fb8ae10a87448aec1a59e49512bd0a4cdc9f8","target":"record","created_at":"2026-05-28T01:04:41Z","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":"14187a97355e550c5fc8b686fade400d1174df56fd08762bb0a75fc4e3aa21db","cross_cats_sorted":["cs.LG","cs.SY","eess.SY"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NI","submitted_at":"2026-05-01T06:43:09Z","title_canon_sha256":"5ab028d0820d5fda486924dfb0e4ff4f873c29200ed1bd7747776eb36f79a868"},"schema_version":"1.0","source":{"id":"2605.00457","kind":"arxiv","version":3}},"canonical_sha256":"3d77fb157f1f2b1ad37355bb0563fc272966517657e12b071ad35f4dc744da4a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3d77fb157f1f2b1ad37355bb0563fc272966517657e12b071ad35f4dc744da4a","first_computed_at":"2026-05-28T01:04:41.257608Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-28T01:04:41.257608Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ns1hRP59UaiY7GgbDJuHNU78Irgp32DAPuHxYIC/wM+egVHBr6Jb/Kx7EeiJ9ZBVmv0q7E6yVzw/zbiW0T+2CQ==","signature_status":"signed_v1","signed_at":"2026-05-28T01:04:41.258116Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.00457","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6b4f9db2baaad45783983c80e73fb8ae10a87448aec1a59e49512bd0a4cdc9f8","sha256:dfa43e8d405b47cf2350a258c1cce580ad2a4512f028c542e4a2711aeb259198"],"state_sha256":"6aeb63171eed4fe215a35b8723d034d3d9018b78655346afa40ed2df79b2b1bd"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"30BApG4pqIkQBNXjs68IKxKVR1KozkrvEzTL6R4HkurODfUw8Yeoazkd1rCITUvE/Yh1FjGIPaNtDfTGqiMEAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-07T07:15:41.408793Z","bundle_sha256":"627fa874c21c9f9143395b142ea25f5b2eee05bd023abd2a16da03cd621934b5"}}