{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:SLWNKN7ZBXG6MWIYQQHKI4UZNH","short_pith_number":"pith:SLWNKN7Z","schema_version":"1.0","canonical_sha256":"92ecd537f90dcde65918840ea4729969c456a351306f7b34b2d55818986fa291","source":{"kind":"arxiv","id":"1812.01571","version":2},"attestation_state":"computed","paper":{"title":"Multilevel MIMO Detection with Deep Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.IT"],"primary_cat":"cs.IT","authors_text":"Joseph J. Boutros, Lo\\\"ic Brunel, Philippe Ciblat, Vincent Corlay","submitted_at":"2018-12-04T18:22:50Z","abstract_excerpt":"A quasi-static flat multiple-antenna channel is considered. We show how real multilevel modulation symbols can be detected via deep neural networks. A multi-plateau sigmoid function is introduced. Then, after showing the DNN architecture for detection, we propose a twin-network neural structure. Batch size and training statistics for efficient learning are investigated. Near-Maximum-Likelihood performance with a relatively reasonable number of parameters is achieved."},"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":"1812.01571","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2018-12-04T18:22:50Z","cross_cats_sorted":["cs.LG","math.IT"],"title_canon_sha256":"2a048ab1e1b421aef56e6e68936e48003820311c899a1f3c3ed25fbe491b42a8","abstract_canon_sha256":"e90b93e2163406d5e4bbd97adfd35fdf5da19a120e6324da46aa1a6c391697e6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:54:02.309213Z","signature_b64":"Kgmk23YXTRTjnkGglHmueHodr/Bdn1AdN8azR3gMh7wH8HlYUFJKaRdG58sb/dvVIplMYxFXvYKObqFlxVHbBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"92ecd537f90dcde65918840ea4729969c456a351306f7b34b2d55818986fa291","last_reissued_at":"2026-05-17T23:54:02.308761Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:54:02.308761Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multilevel MIMO Detection with Deep Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.IT"],"primary_cat":"cs.IT","authors_text":"Joseph J. Boutros, Lo\\\"ic Brunel, Philippe Ciblat, Vincent Corlay","submitted_at":"2018-12-04T18:22:50Z","abstract_excerpt":"A quasi-static flat multiple-antenna channel is considered. We show how real multilevel modulation symbols can be detected via deep neural networks. A multi-plateau sigmoid function is introduced. Then, after showing the DNN architecture for detection, we propose a twin-network neural structure. Batch size and training statistics for efficient learning are investigated. Near-Maximum-Likelihood performance with a relatively reasonable number of parameters is achieved."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.01571","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"1812.01571","created_at":"2026-05-17T23:54:02.308829+00:00"},{"alias_kind":"arxiv_version","alias_value":"1812.01571v2","created_at":"2026-05-17T23:54:02.308829+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.01571","created_at":"2026-05-17T23:54:02.308829+00:00"},{"alias_kind":"pith_short_12","alias_value":"SLWNKN7ZBXG6","created_at":"2026-05-18T12:32:53.628368+00:00"},{"alias_kind":"pith_short_16","alias_value":"SLWNKN7ZBXG6MWIY","created_at":"2026-05-18T12:32:53.628368+00:00"},{"alias_kind":"pith_short_8","alias_value":"SLWNKN7Z","created_at":"2026-05-18T12:32:53.628368+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/SLWNKN7ZBXG6MWIYQQHKI4UZNH","json":"https://pith.science/pith/SLWNKN7ZBXG6MWIYQQHKI4UZNH.json","graph_json":"https://pith.science/api/pith-number/SLWNKN7ZBXG6MWIYQQHKI4UZNH/graph.json","events_json":"https://pith.science/api/pith-number/SLWNKN7ZBXG6MWIYQQHKI4UZNH/events.json","paper":"https://pith.science/paper/SLWNKN7Z"},"agent_actions":{"view_html":"https://pith.science/pith/SLWNKN7ZBXG6MWIYQQHKI4UZNH","download_json":"https://pith.science/pith/SLWNKN7ZBXG6MWIYQQHKI4UZNH.json","view_paper":"https://pith.science/paper/SLWNKN7Z","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1812.01571&json=true","fetch_graph":"https://pith.science/api/pith-number/SLWNKN7ZBXG6MWIYQQHKI4UZNH/graph.json","fetch_events":"https://pith.science/api/pith-number/SLWNKN7ZBXG6MWIYQQHKI4UZNH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SLWNKN7ZBXG6MWIYQQHKI4UZNH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SLWNKN7ZBXG6MWIYQQHKI4UZNH/action/storage_attestation","attest_author":"https://pith.science/pith/SLWNKN7ZBXG6MWIYQQHKI4UZNH/action/author_attestation","sign_citation":"https://pith.science/pith/SLWNKN7ZBXG6MWIYQQHKI4UZNH/action/citation_signature","submit_replication":"https://pith.science/pith/SLWNKN7ZBXG6MWIYQQHKI4UZNH/action/replication_record"}},"created_at":"2026-05-17T23:54:02.308829+00:00","updated_at":"2026-05-17T23:54:02.308829+00:00"}