{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:BXPSHKXD2M7CIA7I4A6RTIZHSG","short_pith_number":"pith:BXPSHKXD","schema_version":"1.0","canonical_sha256":"0ddf23aae3d33e2403e8e03d19a32791bea39b90b4279d05a674dec6a7669fd6","source":{"kind":"arxiv","id":"2409.14031","version":3},"attestation_state":"computed","paper":{"title":"Maximum-Likelihood Estimation Based on Diffusion Model For Wireless Communications","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Changyuan Zhao, Dong In Kim, Dusit Niyato, Hongyang Du, Jiacheng Wang, Ruichen Zhang","submitted_at":"2024-09-21T06:21:36Z","abstract_excerpt":"Generative Artificial Intelligence (GenAI) models, with their powerful feature learning capabilities, have been applied in many fields. In mobile wireless communications, GenAI can dynamically optimize the network to enhance the user experience. Especially in signal detection and channel estimation tasks, due to digital signals following a certain random distribution, GenAI models can fully utilize their distribution learning characteristics. For example, diffusion models (DMs) and normalized flow models have been applied to related tasks. However, since the DM cannot guarantee that the genera"},"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":"2409.14031","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SP","submitted_at":"2024-09-21T06:21:36Z","cross_cats_sorted":[],"title_canon_sha256":"2eae2d91923a01a1ccd1ab01c0dcf24e6bd8783b62c2d1d973ebd7b9ebd785ef","abstract_canon_sha256":"425b0db20340253f4e1dc99db39c7b380b78f3bf1a9c29b3a73a5370a2ff8491"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:59:07.601876Z","signature_b64":"A7e5wCX/VyiJ8X8T7MyiaD7r+JN8UrIu2yrPHkJiJg/QGq+y6IyzbUsFgXnlm+kbIXfD0FsZVGoQL407Cn9qAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0ddf23aae3d33e2403e8e03d19a32791bea39b90b4279d05a674dec6a7669fd6","last_reissued_at":"2026-07-05T11:59:07.601388Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:59:07.601388Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Maximum-Likelihood Estimation Based on Diffusion Model For Wireless Communications","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Changyuan Zhao, Dong In Kim, Dusit Niyato, Hongyang Du, Jiacheng Wang, Ruichen Zhang","submitted_at":"2024-09-21T06:21:36Z","abstract_excerpt":"Generative Artificial Intelligence (GenAI) models, with their powerful feature learning capabilities, have been applied in many fields. In mobile wireless communications, GenAI can dynamically optimize the network to enhance the user experience. Especially in signal detection and channel estimation tasks, due to digital signals following a certain random distribution, GenAI models can fully utilize their distribution learning characteristics. For example, diffusion models (DMs) and normalized flow models have been applied to related tasks. However, since the DM cannot guarantee that the genera"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.14031","kind":"arxiv","version":3},"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/2409.14031/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":"2409.14031","created_at":"2026-07-05T11:59:07.601447+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.14031v3","created_at":"2026-07-05T11:59:07.601447+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.14031","created_at":"2026-07-05T11:59:07.601447+00:00"},{"alias_kind":"pith_short_12","alias_value":"BXPSHKXD2M7C","created_at":"2026-07-05T11:59:07.601447+00:00"},{"alias_kind":"pith_short_16","alias_value":"BXPSHKXD2M7CIA7I","created_at":"2026-07-05T11:59:07.601447+00:00"},{"alias_kind":"pith_short_8","alias_value":"BXPSHKXD","created_at":"2026-07-05T11:59:07.601447+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.01589","citing_title":"Censored Sampling for Topology Design: Guiding Diffusion with Human Preferences","ref_index":18,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BXPSHKXD2M7CIA7I4A6RTIZHSG","json":"https://pith.science/pith/BXPSHKXD2M7CIA7I4A6RTIZHSG.json","graph_json":"https://pith.science/api/pith-number/BXPSHKXD2M7CIA7I4A6RTIZHSG/graph.json","events_json":"https://pith.science/api/pith-number/BXPSHKXD2M7CIA7I4A6RTIZHSG/events.json","paper":"https://pith.science/paper/BXPSHKXD"},"agent_actions":{"view_html":"https://pith.science/pith/BXPSHKXD2M7CIA7I4A6RTIZHSG","download_json":"https://pith.science/pith/BXPSHKXD2M7CIA7I4A6RTIZHSG.json","view_paper":"https://pith.science/paper/BXPSHKXD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.14031&json=true","fetch_graph":"https://pith.science/api/pith-number/BXPSHKXD2M7CIA7I4A6RTIZHSG/graph.json","fetch_events":"https://pith.science/api/pith-number/BXPSHKXD2M7CIA7I4A6RTIZHSG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BXPSHKXD2M7CIA7I4A6RTIZHSG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BXPSHKXD2M7CIA7I4A6RTIZHSG/action/storage_attestation","attest_author":"https://pith.science/pith/BXPSHKXD2M7CIA7I4A6RTIZHSG/action/author_attestation","sign_citation":"https://pith.science/pith/BXPSHKXD2M7CIA7I4A6RTIZHSG/action/citation_signature","submit_replication":"https://pith.science/pith/BXPSHKXD2M7CIA7I4A6RTIZHSG/action/replication_record"}},"created_at":"2026-07-05T11:59:07.601447+00:00","updated_at":"2026-07-05T11:59:07.601447+00:00"}