{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:ZQOKHMXZFVAARHHRCBSLQPMT6G","short_pith_number":"pith:ZQOKHMXZ","schema_version":"1.0","canonical_sha256":"cc1ca3b2f92d40089cf11064b83d93f196f7f6b039998b744344d4f252bc5454","source":{"kind":"arxiv","id":"1806.10333","version":2},"attestation_state":"computed","paper":{"title":"A Generalized Data Representation and Training-Performance Analysis for Deep Learning-Based Communications Systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Liang Wu, Xiao Chen, Zaichen Zhang","submitted_at":"2018-06-27T08:17:51Z","abstract_excerpt":"Deep learning (DL)-based autoencoder is a potential architecture to implement end-to-end communication systems. In this letter, we first give a brief introduction to the autoencoder-represented communication system. Then, we propose a novel generalized data representation (GDR) aiming to improve the data rate of DL-based communication systems. Finally, simulation results show that the proposed GDR scheme has lower training complexity, comparable block error rate performance and higher channel capacity than the conventional one-hot vector scheme. Furthermore, we investigate the effect of signal"},"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":"1806.10333","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2018-06-27T08:17:51Z","cross_cats_sorted":["math.IT"],"title_canon_sha256":"9e958cc46f451ec2bad2f5ebf34bc1feda1b1de0bb59ad75d00aeb644143e20f","abstract_canon_sha256":"3773b0a0bc1b66300bfb20cb85cde3b88bf56abe7ebaa843c325f3a3738d15f2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:11:23.116131Z","signature_b64":"5sqP6nfVlQLfwE+J8m1o1kn5DZST1wQclmX117nDwNINzBJG951jrGB14YZ1qS6hqGAROsSb4QQzoxccIvXUBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cc1ca3b2f92d40089cf11064b83d93f196f7f6b039998b744344d4f252bc5454","last_reissued_at":"2026-05-18T00:11:23.115577Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:11:23.115577Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Generalized Data Representation and Training-Performance Analysis for Deep Learning-Based Communications Systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Liang Wu, Xiao Chen, Zaichen Zhang","submitted_at":"2018-06-27T08:17:51Z","abstract_excerpt":"Deep learning (DL)-based autoencoder is a potential architecture to implement end-to-end communication systems. In this letter, we first give a brief introduction to the autoencoder-represented communication system. Then, we propose a novel generalized data representation (GDR) aiming to improve the data rate of DL-based communication systems. Finally, simulation results show that the proposed GDR scheme has lower training complexity, comparable block error rate performance and higher channel capacity than the conventional one-hot vector scheme. Furthermore, we investigate the effect of signal"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.10333","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":"1806.10333","created_at":"2026-05-18T00:11:23.115664+00:00"},{"alias_kind":"arxiv_version","alias_value":"1806.10333v2","created_at":"2026-05-18T00:11:23.115664+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.10333","created_at":"2026-05-18T00:11:23.115664+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZQOKHMXZFVAA","created_at":"2026-05-18T12:33:07.085635+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZQOKHMXZFVAARHHR","created_at":"2026-05-18T12:33:07.085635+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZQOKHMXZ","created_at":"2026-05-18T12:33:07.085635+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/ZQOKHMXZFVAARHHRCBSLQPMT6G","json":"https://pith.science/pith/ZQOKHMXZFVAARHHRCBSLQPMT6G.json","graph_json":"https://pith.science/api/pith-number/ZQOKHMXZFVAARHHRCBSLQPMT6G/graph.json","events_json":"https://pith.science/api/pith-number/ZQOKHMXZFVAARHHRCBSLQPMT6G/events.json","paper":"https://pith.science/paper/ZQOKHMXZ"},"agent_actions":{"view_html":"https://pith.science/pith/ZQOKHMXZFVAARHHRCBSLQPMT6G","download_json":"https://pith.science/pith/ZQOKHMXZFVAARHHRCBSLQPMT6G.json","view_paper":"https://pith.science/paper/ZQOKHMXZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1806.10333&json=true","fetch_graph":"https://pith.science/api/pith-number/ZQOKHMXZFVAARHHRCBSLQPMT6G/graph.json","fetch_events":"https://pith.science/api/pith-number/ZQOKHMXZFVAARHHRCBSLQPMT6G/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZQOKHMXZFVAARHHRCBSLQPMT6G/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZQOKHMXZFVAARHHRCBSLQPMT6G/action/storage_attestation","attest_author":"https://pith.science/pith/ZQOKHMXZFVAARHHRCBSLQPMT6G/action/author_attestation","sign_citation":"https://pith.science/pith/ZQOKHMXZFVAARHHRCBSLQPMT6G/action/citation_signature","submit_replication":"https://pith.science/pith/ZQOKHMXZFVAARHHRCBSLQPMT6G/action/replication_record"}},"created_at":"2026-05-18T00:11:23.115664+00:00","updated_at":"2026-05-18T00:11:23.115664+00:00"}