{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:MCES6RIWTMMY6A3W2XYMZ7URYF","short_pith_number":"pith:MCES6RIW","schema_version":"1.0","canonical_sha256":"60892f45169b198f0376d5f0ccfe91c14e318b64e8fa1e5c75ee8199038856d1","source":{"kind":"arxiv","id":"2406.18306","version":13},"attestation_state":"computed","paper":{"title":"Neural Network-Based Intelligent Reflecting Surface Assisted Direction of Arrival Estimation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Mahmoud Farhang, Yasin Azhdari","submitted_at":"2024-06-26T12:45:48Z","abstract_excerpt":"Direction-of-Arrival (DoA) estimation assisted with an Intelligent Reflecting Surface (IRS) is crucial for various wireless applications, especially in challenging Non-Line-of-Sight (NLoS) environments. This paper presents a novel neural network-based architecture to address this challenge.\n  The key innovation is the introduction of a dedicated, learnable IRS layer integrated within a carefully designed end-to-end system established upon the physical and geometrical basis of the problem. Unlike conventional neural network layers, this specific one incorporates block diagonal sinusoidal weight"},"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":"2406.18306","kind":"arxiv","version":13},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SP","submitted_at":"2024-06-26T12:45:48Z","cross_cats_sorted":[],"title_canon_sha256":"37313e101942b36010b866f54e7671d2032a4af55620555af00e9eb1fa2dfac6","abstract_canon_sha256":"e4e486b52cf0cd54a7392ba4edf7ec03c7c72fa9fe92a947f191edfe562777dc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:07:02.813660Z","signature_b64":"J8D+GMbEWYCac/ZTdIRj95P+vuh+h/zbpNjZw9E+FygMDlaDRnI6OmdH3zIaAgms56Ej3aLkqAMwTMte44oxBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"60892f45169b198f0376d5f0ccfe91c14e318b64e8fa1e5c75ee8199038856d1","last_reissued_at":"2026-07-05T11:07:02.813209Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:07:02.813209Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Neural Network-Based Intelligent Reflecting Surface Assisted Direction of Arrival Estimation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Mahmoud Farhang, Yasin Azhdari","submitted_at":"2024-06-26T12:45:48Z","abstract_excerpt":"Direction-of-Arrival (DoA) estimation assisted with an Intelligent Reflecting Surface (IRS) is crucial for various wireless applications, especially in challenging Non-Line-of-Sight (NLoS) environments. This paper presents a novel neural network-based architecture to address this challenge.\n  The key innovation is the introduction of a dedicated, learnable IRS layer integrated within a carefully designed end-to-end system established upon the physical and geometrical basis of the problem. Unlike conventional neural network layers, this specific one incorporates block diagonal sinusoidal weight"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.18306","kind":"arxiv","version":13},"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/2406.18306/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":"2406.18306","created_at":"2026-07-05T11:07:02.813262+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.18306v13","created_at":"2026-07-05T11:07:02.813262+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.18306","created_at":"2026-07-05T11:07:02.813262+00:00"},{"alias_kind":"pith_short_12","alias_value":"MCES6RIWTMMY","created_at":"2026-07-05T11:07:02.813262+00:00"},{"alias_kind":"pith_short_16","alias_value":"MCES6RIWTMMY6A3W","created_at":"2026-07-05T11:07:02.813262+00:00"},{"alias_kind":"pith_short_8","alias_value":"MCES6RIW","created_at":"2026-07-05T11:07:02.813262+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/MCES6RIWTMMY6A3W2XYMZ7URYF","json":"https://pith.science/pith/MCES6RIWTMMY6A3W2XYMZ7URYF.json","graph_json":"https://pith.science/api/pith-number/MCES6RIWTMMY6A3W2XYMZ7URYF/graph.json","events_json":"https://pith.science/api/pith-number/MCES6RIWTMMY6A3W2XYMZ7URYF/events.json","paper":"https://pith.science/paper/MCES6RIW"},"agent_actions":{"view_html":"https://pith.science/pith/MCES6RIWTMMY6A3W2XYMZ7URYF","download_json":"https://pith.science/pith/MCES6RIWTMMY6A3W2XYMZ7URYF.json","view_paper":"https://pith.science/paper/MCES6RIW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.18306&json=true","fetch_graph":"https://pith.science/api/pith-number/MCES6RIWTMMY6A3W2XYMZ7URYF/graph.json","fetch_events":"https://pith.science/api/pith-number/MCES6RIWTMMY6A3W2XYMZ7URYF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MCES6RIWTMMY6A3W2XYMZ7URYF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MCES6RIWTMMY6A3W2XYMZ7URYF/action/storage_attestation","attest_author":"https://pith.science/pith/MCES6RIWTMMY6A3W2XYMZ7URYF/action/author_attestation","sign_citation":"https://pith.science/pith/MCES6RIWTMMY6A3W2XYMZ7URYF/action/citation_signature","submit_replication":"https://pith.science/pith/MCES6RIWTMMY6A3W2XYMZ7URYF/action/replication_record"}},"created_at":"2026-07-05T11:07:02.813262+00:00","updated_at":"2026-07-05T11:07:02.813262+00:00"}