{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:O4QLBKCUOLE3KD2YXMMHZ4NO2D","short_pith_number":"pith:O4QLBKCU","schema_version":"1.0","canonical_sha256":"7720b0a85472c9b50f58bb187cf1aed0c8118cf315364ff02cb47d304b8b53d1","source":{"kind":"arxiv","id":"2103.11122","version":1},"attestation_state":"computed","paper":{"title":"Model-based Learning Network for 3-D Localization in mmWave Communications","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["eess.SP","math.IT"],"primary_cat":"cs.IT","authors_text":"Bo Gao, Chao-Kai Wen, Jiajia Guo, Jie Yang, Michail Matthaiou, Shi Jin","submitted_at":"2021-03-20T07:33:39Z","abstract_excerpt":"This study considers the joint location and velocity estimation of UE and scatterers in a three-dimensional mmWave CRAN architecture. Several existing works have achieved satisfactory results with neural networks (NNs) for localization. However, the black box NN localization method has limited performance and relies on a prohibitive amount of training data. Thus, we propose a model-based learning network for localization by combining NNs with geometric models. Specifically, we first develop an unbiased WLS estimator by utilizing hybrid delay/angular measurements, which determine the location a"},"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":"2103.11122","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IT","submitted_at":"2021-03-20T07:33:39Z","cross_cats_sorted":["eess.SP","math.IT"],"title_canon_sha256":"b8282108f60c2c1be58751c592fb37cf52179d24d88678d02081e434e8336331","abstract_canon_sha256":"b6329820042150e86f9d6d6748233da1e6fde1ee8d865164144c4c3ca3afc1c4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:24:57.155857Z","signature_b64":"uOr67ocThOowl+7e73ctAQecv5dMJcuRbLk1vNuPywVoKk0ddyfDnsCGKj8Cp87M+cbvFCyk1NhoIwmeWEwqDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7720b0a85472c9b50f58bb187cf1aed0c8118cf315364ff02cb47d304b8b53d1","last_reissued_at":"2026-07-05T02:24:57.155327Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:24:57.155327Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Model-based Learning Network for 3-D Localization in mmWave Communications","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["eess.SP","math.IT"],"primary_cat":"cs.IT","authors_text":"Bo Gao, Chao-Kai Wen, Jiajia Guo, Jie Yang, Michail Matthaiou, Shi Jin","submitted_at":"2021-03-20T07:33:39Z","abstract_excerpt":"This study considers the joint location and velocity estimation of UE and scatterers in a three-dimensional mmWave CRAN architecture. Several existing works have achieved satisfactory results with neural networks (NNs) for localization. However, the black box NN localization method has limited performance and relies on a prohibitive amount of training data. Thus, we propose a model-based learning network for localization by combining NNs with geometric models. Specifically, we first develop an unbiased WLS estimator by utilizing hybrid delay/angular measurements, which determine the location a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.11122","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/2103.11122/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":"2103.11122","created_at":"2026-07-05T02:24:57.155388+00:00"},{"alias_kind":"arxiv_version","alias_value":"2103.11122v1","created_at":"2026-07-05T02:24:57.155388+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.11122","created_at":"2026-07-05T02:24:57.155388+00:00"},{"alias_kind":"pith_short_12","alias_value":"O4QLBKCUOLE3","created_at":"2026-07-05T02:24:57.155388+00:00"},{"alias_kind":"pith_short_16","alias_value":"O4QLBKCUOLE3KD2Y","created_at":"2026-07-05T02:24:57.155388+00:00"},{"alias_kind":"pith_short_8","alias_value":"O4QLBKCU","created_at":"2026-07-05T02:24:57.155388+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/O4QLBKCUOLE3KD2YXMMHZ4NO2D","json":"https://pith.science/pith/O4QLBKCUOLE3KD2YXMMHZ4NO2D.json","graph_json":"https://pith.science/api/pith-number/O4QLBKCUOLE3KD2YXMMHZ4NO2D/graph.json","events_json":"https://pith.science/api/pith-number/O4QLBKCUOLE3KD2YXMMHZ4NO2D/events.json","paper":"https://pith.science/paper/O4QLBKCU"},"agent_actions":{"view_html":"https://pith.science/pith/O4QLBKCUOLE3KD2YXMMHZ4NO2D","download_json":"https://pith.science/pith/O4QLBKCUOLE3KD2YXMMHZ4NO2D.json","view_paper":"https://pith.science/paper/O4QLBKCU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2103.11122&json=true","fetch_graph":"https://pith.science/api/pith-number/O4QLBKCUOLE3KD2YXMMHZ4NO2D/graph.json","fetch_events":"https://pith.science/api/pith-number/O4QLBKCUOLE3KD2YXMMHZ4NO2D/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/O4QLBKCUOLE3KD2YXMMHZ4NO2D/action/timestamp_anchor","attest_storage":"https://pith.science/pith/O4QLBKCUOLE3KD2YXMMHZ4NO2D/action/storage_attestation","attest_author":"https://pith.science/pith/O4QLBKCUOLE3KD2YXMMHZ4NO2D/action/author_attestation","sign_citation":"https://pith.science/pith/O4QLBKCUOLE3KD2YXMMHZ4NO2D/action/citation_signature","submit_replication":"https://pith.science/pith/O4QLBKCUOLE3KD2YXMMHZ4NO2D/action/replication_record"}},"created_at":"2026-07-05T02:24:57.155388+00:00","updated_at":"2026-07-05T02:24:57.155388+00:00"}