{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:OSOT7RTKUZ5BIZWSFI7TDMDQPK","short_pith_number":"pith:OSOT7RTK","schema_version":"1.0","canonical_sha256":"749d3fc66aa67a1466d22a3f31b0707aaa2c4d9520069ddff80b295ec7c78a92","source":{"kind":"arxiv","id":"2411.14052","version":1},"attestation_state":"computed","paper":{"title":"Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Fei Song, Jun Li, Long Shi, Shi Jin, Wen Chen, Zhe Wang","submitted_at":"2024-11-21T11:59:00Z","abstract_excerpt":"In ultra-dense unmanned aerial vehicle (UAV) networks, it is challenging to coordinate the resource allocation and interference management among large-scale UAVs, for providing flexible and efficient service coverage to the ground users (GUs). In this paper, we propose a learning-based resource allocation scheme in an ultra-dense UAV communication network, where the GUs' service demands are time-varying with unknown distributions. We formulate the non-cooperative game among multiple co-channel UAVs as a stochastic game, where each UAV jointly optimizes its trajectory, user association, and dow"},"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":"2411.14052","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SY","submitted_at":"2024-11-21T11:59:00Z","cross_cats_sorted":["cs.SY"],"title_canon_sha256":"2c115ab7fe986f70813b52e0fcf4019a8a4b88b44a4305d1d51135f8e83f2004","abstract_canon_sha256":"1c7657fdb5d8d49d0e52711bf0c44c1d9b87a1537c9f8a361a6c2dcf93895823"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:38:41.001275Z","signature_b64":"+MmNHyjLhhDy82klRgUKIZyHlQec+r88HStzAqKzExV8pS5GLzhlwS/6NJO05zO5aBbm2MBMacw3WHA6/oOpCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"749d3fc66aa67a1466d22a3f31b0707aaa2c4d9520069ddff80b295ec7c78a92","last_reissued_at":"2026-07-05T09:38:41.000856Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:38:41.000856Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Fei Song, Jun Li, Long Shi, Shi Jin, Wen Chen, Zhe Wang","submitted_at":"2024-11-21T11:59:00Z","abstract_excerpt":"In ultra-dense unmanned aerial vehicle (UAV) networks, it is challenging to coordinate the resource allocation and interference management among large-scale UAVs, for providing flexible and efficient service coverage to the ground users (GUs). In this paper, we propose a learning-based resource allocation scheme in an ultra-dense UAV communication network, where the GUs' service demands are time-varying with unknown distributions. We formulate the non-cooperative game among multiple co-channel UAVs as a stochastic game, where each UAV jointly optimizes its trajectory, user association, and dow"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.14052","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/2411.14052/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":"2411.14052","created_at":"2026-07-05T09:38:41.000919+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.14052v1","created_at":"2026-07-05T09:38:41.000919+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.14052","created_at":"2026-07-05T09:38:41.000919+00:00"},{"alias_kind":"pith_short_12","alias_value":"OSOT7RTKUZ5B","created_at":"2026-07-05T09:38:41.000919+00:00"},{"alias_kind":"pith_short_16","alias_value":"OSOT7RTKUZ5BIZWS","created_at":"2026-07-05T09:38:41.000919+00:00"},{"alias_kind":"pith_short_8","alias_value":"OSOT7RTK","created_at":"2026-07-05T09:38:41.000919+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/OSOT7RTKUZ5BIZWSFI7TDMDQPK","json":"https://pith.science/pith/OSOT7RTKUZ5BIZWSFI7TDMDQPK.json","graph_json":"https://pith.science/api/pith-number/OSOT7RTKUZ5BIZWSFI7TDMDQPK/graph.json","events_json":"https://pith.science/api/pith-number/OSOT7RTKUZ5BIZWSFI7TDMDQPK/events.json","paper":"https://pith.science/paper/OSOT7RTK"},"agent_actions":{"view_html":"https://pith.science/pith/OSOT7RTKUZ5BIZWSFI7TDMDQPK","download_json":"https://pith.science/pith/OSOT7RTKUZ5BIZWSFI7TDMDQPK.json","view_paper":"https://pith.science/paper/OSOT7RTK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.14052&json=true","fetch_graph":"https://pith.science/api/pith-number/OSOT7RTKUZ5BIZWSFI7TDMDQPK/graph.json","fetch_events":"https://pith.science/api/pith-number/OSOT7RTKUZ5BIZWSFI7TDMDQPK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OSOT7RTKUZ5BIZWSFI7TDMDQPK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OSOT7RTKUZ5BIZWSFI7TDMDQPK/action/storage_attestation","attest_author":"https://pith.science/pith/OSOT7RTKUZ5BIZWSFI7TDMDQPK/action/author_attestation","sign_citation":"https://pith.science/pith/OSOT7RTKUZ5BIZWSFI7TDMDQPK/action/citation_signature","submit_replication":"https://pith.science/pith/OSOT7RTKUZ5BIZWSFI7TDMDQPK/action/replication_record"}},"created_at":"2026-07-05T09:38:41.000919+00:00","updated_at":"2026-07-05T09:38:41.000919+00:00"}