{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:PGRY7WBKXJG43IJD3TYDRHJS4R","short_pith_number":"pith:PGRY7WBK","schema_version":"1.0","canonical_sha256":"79a38fd82aba4dcda123dcf0389d32e4476d46327e53079b11e5ab8d9cbc1ca2","source":{"kind":"arxiv","id":"1808.08015","version":1},"attestation_state":"computed","paper":{"title":"An Enhanced SCMA Detector Enabled by Deep Neural Network","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Chao Lu, Hong Shen, Hua Zhang, Wei Xu, Xiaohu You","submitted_at":"2018-08-24T06:24:24Z","abstract_excerpt":"In this paper, we propose a learning approach for sparse code multiple access (SCMA) signal detection by using a deep neural network via unfolding the procedure of message passing algorithm (MPA). The MPA can be converted to a sparsely connected neural network if we treat the weights as the parameters of a neural network. The neural network can be trained off-line and then deployed for online detection. By further refining the network weights corresponding to the edges of a factor graph, the proposed method achieves a better performance. Moreover, the deep neural network based detection is a c"},"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":"1808.08015","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.IT","submitted_at":"2018-08-24T06:24:24Z","cross_cats_sorted":["math.IT"],"title_canon_sha256":"4ac13cea3856e5525fbc3843ee27dd9ed2dffaed0663f80da2fece15e9927639","abstract_canon_sha256":"67a97d344bd550b59b93a33c17d7610fa34e61264f89774897a268bce471b3a8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:07:22.727354Z","signature_b64":"nnaIdGeC9tT4lsbcRnsXhpZ6Pu9205zD8+zzJI7Cj456IOu0FXKHETw+QzsVjnoK1asdiY4tNXznUNatLPTxAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"79a38fd82aba4dcda123dcf0389d32e4476d46327e53079b11e5ab8d9cbc1ca2","last_reissued_at":"2026-05-18T00:07:22.726820Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:07:22.726820Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An Enhanced SCMA Detector Enabled by Deep Neural Network","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Chao Lu, Hong Shen, Hua Zhang, Wei Xu, Xiaohu You","submitted_at":"2018-08-24T06:24:24Z","abstract_excerpt":"In this paper, we propose a learning approach for sparse code multiple access (SCMA) signal detection by using a deep neural network via unfolding the procedure of message passing algorithm (MPA). The MPA can be converted to a sparsely connected neural network if we treat the weights as the parameters of a neural network. The neural network can be trained off-line and then deployed for online detection. By further refining the network weights corresponding to the edges of a factor graph, the proposed method achieves a better performance. Moreover, the deep neural network based detection is a c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.08015","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":""},"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":"1808.08015","created_at":"2026-05-18T00:07:22.726908+00:00"},{"alias_kind":"arxiv_version","alias_value":"1808.08015v1","created_at":"2026-05-18T00:07:22.726908+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.08015","created_at":"2026-05-18T00:07:22.726908+00:00"},{"alias_kind":"pith_short_12","alias_value":"PGRY7WBKXJG4","created_at":"2026-05-18T12:32:43.782077+00:00"},{"alias_kind":"pith_short_16","alias_value":"PGRY7WBKXJG43IJD","created_at":"2026-05-18T12:32:43.782077+00:00"},{"alias_kind":"pith_short_8","alias_value":"PGRY7WBK","created_at":"2026-05-18T12:32:43.782077+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/PGRY7WBKXJG43IJD3TYDRHJS4R","json":"https://pith.science/pith/PGRY7WBKXJG43IJD3TYDRHJS4R.json","graph_json":"https://pith.science/api/pith-number/PGRY7WBKXJG43IJD3TYDRHJS4R/graph.json","events_json":"https://pith.science/api/pith-number/PGRY7WBKXJG43IJD3TYDRHJS4R/events.json","paper":"https://pith.science/paper/PGRY7WBK"},"agent_actions":{"view_html":"https://pith.science/pith/PGRY7WBKXJG43IJD3TYDRHJS4R","download_json":"https://pith.science/pith/PGRY7WBKXJG43IJD3TYDRHJS4R.json","view_paper":"https://pith.science/paper/PGRY7WBK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1808.08015&json=true","fetch_graph":"https://pith.science/api/pith-number/PGRY7WBKXJG43IJD3TYDRHJS4R/graph.json","fetch_events":"https://pith.science/api/pith-number/PGRY7WBKXJG43IJD3TYDRHJS4R/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PGRY7WBKXJG43IJD3TYDRHJS4R/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PGRY7WBKXJG43IJD3TYDRHJS4R/action/storage_attestation","attest_author":"https://pith.science/pith/PGRY7WBKXJG43IJD3TYDRHJS4R/action/author_attestation","sign_citation":"https://pith.science/pith/PGRY7WBKXJG43IJD3TYDRHJS4R/action/citation_signature","submit_replication":"https://pith.science/pith/PGRY7WBKXJG43IJD3TYDRHJS4R/action/replication_record"}},"created_at":"2026-05-18T00:07:22.726908+00:00","updated_at":"2026-05-18T00:07:22.726908+00:00"}