{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:PGRY7WBKXJG43IJD3TYDRHJS4R","short_pith_number":"pith:PGRY7WBK","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"},"canonical_sha256":"79a38fd82aba4dcda123dcf0389d32e4476d46327e53079b11e5ab8d9cbc1ca2","source":{"kind":"arxiv","id":"1808.08015","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1808.08015","created_at":"2026-05-18T00:07:22Z"},{"alias_kind":"arxiv_version","alias_value":"1808.08015v1","created_at":"2026-05-18T00:07:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.08015","created_at":"2026-05-18T00:07:22Z"},{"alias_kind":"pith_short_12","alias_value":"PGRY7WBKXJG4","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_16","alias_value":"PGRY7WBKXJG43IJD","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_8","alias_value":"PGRY7WBK","created_at":"2026-05-18T12:32:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:PGRY7WBKXJG43IJD3TYDRHJS4R","target":"record","payload":{"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"},"canonical_sha256":"79a38fd82aba4dcda123dcf0389d32e4476d46327e53079b11e5ab8d9cbc1ca2","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"},"source_kind":"arxiv","source_id":"1808.08015","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:07:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hGbHBveBgq73J/5x7P42IPmv81tQ2l3zRPZTY8xcUOgbZZdCQUVGDDwEVgf3QRZCDpxizkV/fORZx2MuOKADBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-20T09:30:31.792476Z"},"content_sha256":"f13922588de5e8074f353c08f1bef0169fc084acb53814378acffa2c13090b0c","schema_version":"1.0","event_id":"sha256:f13922588de5e8074f353c08f1bef0169fc084acb53814378acffa2c13090b0c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:PGRY7WBKXJG43IJD3TYDRHJS4R","target":"graph","payload":{"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:07:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"P2IHFuNTuJrrT33m6D1b2IQmq6BYrW/R51PJzPlI0o/LkzJoGTGzghE0Ows5X6xjZFKquPkdxIhhWFUL2kd9Dg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-20T09:30:31.792828Z"},"content_sha256":"623bcc5d59f7eb030c00273eadc78a360d22dbffce42936b8e7112c5f37f05c6","schema_version":"1.0","event_id":"sha256:623bcc5d59f7eb030c00273eadc78a360d22dbffce42936b8e7112c5f37f05c6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PGRY7WBKXJG43IJD3TYDRHJS4R/bundle.json","state_url":"https://pith.science/pith/PGRY7WBKXJG43IJD3TYDRHJS4R/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PGRY7WBKXJG43IJD3TYDRHJS4R/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-20T09:30:31Z","links":{"resolver":"https://pith.science/pith/PGRY7WBKXJG43IJD3TYDRHJS4R","bundle":"https://pith.science/pith/PGRY7WBKXJG43IJD3TYDRHJS4R/bundle.json","state":"https://pith.science/pith/PGRY7WBKXJG43IJD3TYDRHJS4R/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PGRY7WBKXJG43IJD3TYDRHJS4R/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:PGRY7WBKXJG43IJD3TYDRHJS4R","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"67a97d344bd550b59b93a33c17d7610fa34e61264f89774897a268bce471b3a8","cross_cats_sorted":["math.IT"],"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.IT","submitted_at":"2018-08-24T06:24:24Z","title_canon_sha256":"4ac13cea3856e5525fbc3843ee27dd9ed2dffaed0663f80da2fece15e9927639"},"schema_version":"1.0","source":{"id":"1808.08015","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1808.08015","created_at":"2026-05-18T00:07:22Z"},{"alias_kind":"arxiv_version","alias_value":"1808.08015v1","created_at":"2026-05-18T00:07:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.08015","created_at":"2026-05-18T00:07:22Z"},{"alias_kind":"pith_short_12","alias_value":"PGRY7WBKXJG4","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_16","alias_value":"PGRY7WBKXJG43IJD","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_8","alias_value":"PGRY7WBK","created_at":"2026-05-18T12:32:43Z"}],"graph_snapshots":[{"event_id":"sha256:623bcc5d59f7eb030c00273eadc78a360d22dbffce42936b8e7112c5f37f05c6","target":"graph","created_at":"2026-05-18T00:07:22Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"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","authors_text":"Chao Lu, Hong Shen, Hua Zhang, Wei Xu, Xiaohu You","cross_cats":["math.IT"],"headline":"","license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.IT","submitted_at":"2018-08-24T06:24:24Z","title":"An Enhanced SCMA Detector Enabled by Deep Neural Network"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.08015","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:f13922588de5e8074f353c08f1bef0169fc084acb53814378acffa2c13090b0c","target":"record","created_at":"2026-05-18T00:07:22Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"67a97d344bd550b59b93a33c17d7610fa34e61264f89774897a268bce471b3a8","cross_cats_sorted":["math.IT"],"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.IT","submitted_at":"2018-08-24T06:24:24Z","title_canon_sha256":"4ac13cea3856e5525fbc3843ee27dd9ed2dffaed0663f80da2fece15e9927639"},"schema_version":"1.0","source":{"id":"1808.08015","kind":"arxiv","version":1}},"canonical_sha256":"79a38fd82aba4dcda123dcf0389d32e4476d46327e53079b11e5ab8d9cbc1ca2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"79a38fd82aba4dcda123dcf0389d32e4476d46327e53079b11e5ab8d9cbc1ca2","first_computed_at":"2026-05-18T00:07:22.726820Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:07:22.726820Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"nnaIdGeC9tT4lsbcRnsXhpZ6Pu9205zD8+zzJI7Cj456IOu0FXKHETw+QzsVjnoK1asdiY4tNXznUNatLPTxAA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:07:22.727354Z","signed_message":"canonical_sha256_bytes"},"source_id":"1808.08015","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f13922588de5e8074f353c08f1bef0169fc084acb53814378acffa2c13090b0c","sha256:623bcc5d59f7eb030c00273eadc78a360d22dbffce42936b8e7112c5f37f05c6"],"state_sha256":"684975276f7ebd64bb3254454bcdc06a7439fb4e2e39b7953bd85d6de2230a32"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fad/1PQ2kYSTnzoHxRrbu5sBGg9uM+jFILy/bKDpUtDQyrrgNKyn3Jt8zQUo7rNZBDeR3dTUKIeQaskQCeJfCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-20T09:30:31.794763Z","bundle_sha256":"7231b01673a18da65ee88d69376cadade51047c64a6c66ad4c988a6a5b0d1f14"}}