{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:5U5JNUPZWLSAXK3I73EO6EBABN","short_pith_number":"pith:5U5JNUPZ","schema_version":"1.0","canonical_sha256":"ed3a96d1f9b2e40bab68fec8ef10200b4431211269b3c0f4ecb8c00a4b6284c2","source":{"kind":"arxiv","id":"1906.06563","version":1},"attestation_state":"computed","paper":{"title":"An End-to-End Block Autoencoder For Physical Layer Based On Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.SP","math.IT"],"primary_cat":"cs.IT","authors_text":"Huarui Yin, Li Chen, Tianjie Mu, Weidong Wang, Xiaohui Chen","submitted_at":"2019-06-15T13:31:17Z","abstract_excerpt":"Deep Learning has been widely applied in the area of image processing and natural language processing. In this paper, we propose an end-to-end communication structure based on autoencoder where the transceiver can be optimized jointly. A neural network roles as a combination of channel encoder and modulator. In order to deal with input sequences parallelly, we introduce block scheme, which means that the autoencoder divides the input sequence into a series of blocks. Each block contains fixed number of bits for encoding and modulating operation. Through training, the proposed system is able to"},"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":"1906.06563","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2019-06-15T13:31:17Z","cross_cats_sorted":["eess.SP","math.IT"],"title_canon_sha256":"54c79067e11639eb02e4b29ee122335c2cf16b7c77c51ab3251a86ed30e70eb6","abstract_canon_sha256":"b151473e33a8fb4a89b03509efa3592f2c2b38a8a057889d36ddc6852ad52180"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:43:13.330733Z","signature_b64":"l3kXcug4wHFjgKfAGkw3V7AoYmTGZFBZ1F9cW4PuEzj1ukSykO19f1Wg3d1wRypE3naVefkXB0nL2eh1JTXrAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ed3a96d1f9b2e40bab68fec8ef10200b4431211269b3c0f4ecb8c00a4b6284c2","last_reissued_at":"2026-05-17T23:43:13.330271Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:43:13.330271Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An End-to-End Block Autoencoder For Physical Layer Based On Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.SP","math.IT"],"primary_cat":"cs.IT","authors_text":"Huarui Yin, Li Chen, Tianjie Mu, Weidong Wang, Xiaohui Chen","submitted_at":"2019-06-15T13:31:17Z","abstract_excerpt":"Deep Learning has been widely applied in the area of image processing and natural language processing. In this paper, we propose an end-to-end communication structure based on autoencoder where the transceiver can be optimized jointly. A neural network roles as a combination of channel encoder and modulator. In order to deal with input sequences parallelly, we introduce block scheme, which means that the autoencoder divides the input sequence into a series of blocks. Each block contains fixed number of bits for encoding and modulating operation. Through training, the proposed system is able to"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.06563","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":"1906.06563","created_at":"2026-05-17T23:43:13.330356+00:00"},{"alias_kind":"arxiv_version","alias_value":"1906.06563v1","created_at":"2026-05-17T23:43:13.330356+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.06563","created_at":"2026-05-17T23:43:13.330356+00:00"},{"alias_kind":"pith_short_12","alias_value":"5U5JNUPZWLSA","created_at":"2026-05-18T12:33:10.108867+00:00"},{"alias_kind":"pith_short_16","alias_value":"5U5JNUPZWLSAXK3I","created_at":"2026-05-18T12:33:10.108867+00:00"},{"alias_kind":"pith_short_8","alias_value":"5U5JNUPZ","created_at":"2026-05-18T12:33:10.108867+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/5U5JNUPZWLSAXK3I73EO6EBABN","json":"https://pith.science/pith/5U5JNUPZWLSAXK3I73EO6EBABN.json","graph_json":"https://pith.science/api/pith-number/5U5JNUPZWLSAXK3I73EO6EBABN/graph.json","events_json":"https://pith.science/api/pith-number/5U5JNUPZWLSAXK3I73EO6EBABN/events.json","paper":"https://pith.science/paper/5U5JNUPZ"},"agent_actions":{"view_html":"https://pith.science/pith/5U5JNUPZWLSAXK3I73EO6EBABN","download_json":"https://pith.science/pith/5U5JNUPZWLSAXK3I73EO6EBABN.json","view_paper":"https://pith.science/paper/5U5JNUPZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1906.06563&json=true","fetch_graph":"https://pith.science/api/pith-number/5U5JNUPZWLSAXK3I73EO6EBABN/graph.json","fetch_events":"https://pith.science/api/pith-number/5U5JNUPZWLSAXK3I73EO6EBABN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5U5JNUPZWLSAXK3I73EO6EBABN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5U5JNUPZWLSAXK3I73EO6EBABN/action/storage_attestation","attest_author":"https://pith.science/pith/5U5JNUPZWLSAXK3I73EO6EBABN/action/author_attestation","sign_citation":"https://pith.science/pith/5U5JNUPZWLSAXK3I73EO6EBABN/action/citation_signature","submit_replication":"https://pith.science/pith/5U5JNUPZWLSAXK3I73EO6EBABN/action/replication_record"}},"created_at":"2026-05-17T23:43:13.330356+00:00","updated_at":"2026-05-17T23:43:13.330356+00:00"}