{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:IPKTZBFLN6HT3POGQKTZQL5J3T","short_pith_number":"pith:IPKTZBFL","schema_version":"1.0","canonical_sha256":"43d53c84ab6f8f3dbdc682a7982fa9dcc5c5d963ca798ddb5ffbbc407d1898b6","source":{"kind":"arxiv","id":"1911.13055","version":2},"attestation_state":"computed","paper":{"title":"Trainable Communication Systems: Concepts and Prototype","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.SP","math.IT"],"primary_cat":"cs.IT","authors_text":"Fay\\c{c}al Ait Aoudia, Jakob Hoydis, Maximilian Stark, Sebastian Cammerer, Sebastian D\\\"orner, Stephan ten Brink","submitted_at":"2019-11-29T11:06:53Z","abstract_excerpt":"We consider a trainable point-to-point communication system, where both transmitter and receiver are implemented as neural networks (NNs), and demonstrate that training on the bit-wise mutual information (BMI) allows seamless integration with practical bit-metric decoding (BMD) receivers, as well as joint optimization of constellation shaping and labeling. Moreover, we present a fully differentiable neural iterative demapping and decoding (IDD) structure which achieves significant gains on additive white Gaussian noise (AWGN) channels using a standard 802.11n low-density parity-check (LDPC) co"},"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":"1911.13055","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2019-11-29T11:06:53Z","cross_cats_sorted":["eess.SP","math.IT"],"title_canon_sha256":"decd1cf761a432dd1c5bb0a7adff72843df72ea83cff1584cae1b4feae6860c6","abstract_canon_sha256":"74899426ea8aa6a5121181572a4ecb1abec5bd7d70aad54e1fff36c20fbd8c41"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:08:10.905898Z","signature_b64":"G2x9968nE9kjj/pUXY1he7X+NyRU5HzIgsuQhfhvc549CUogl7bbX+ZDTsbSZuWZbva5mC4c4Fti++0h3NsfCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"43d53c84ab6f8f3dbdc682a7982fa9dcc5c5d963ca798ddb5ffbbc407d1898b6","last_reissued_at":"2026-07-05T01:08:10.905516Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:08:10.905516Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Trainable Communication Systems: Concepts and Prototype","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.SP","math.IT"],"primary_cat":"cs.IT","authors_text":"Fay\\c{c}al Ait Aoudia, Jakob Hoydis, Maximilian Stark, Sebastian Cammerer, Sebastian D\\\"orner, Stephan ten Brink","submitted_at":"2019-11-29T11:06:53Z","abstract_excerpt":"We consider a trainable point-to-point communication system, where both transmitter and receiver are implemented as neural networks (NNs), and demonstrate that training on the bit-wise mutual information (BMI) allows seamless integration with practical bit-metric decoding (BMD) receivers, as well as joint optimization of constellation shaping and labeling. Moreover, we present a fully differentiable neural iterative demapping and decoding (IDD) structure which achieves significant gains on additive white Gaussian noise (AWGN) channels using a standard 802.11n low-density parity-check (LDPC) co"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1911.13055","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1911.13055/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"1911.13055","created_at":"2026-07-05T01:08:10.905581+00:00"},{"alias_kind":"arxiv_version","alias_value":"1911.13055v2","created_at":"2026-07-05T01:08:10.905581+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1911.13055","created_at":"2026-07-05T01:08:10.905581+00:00"},{"alias_kind":"pith_short_12","alias_value":"IPKTZBFLN6HT","created_at":"2026-07-05T01:08:10.905581+00:00"},{"alias_kind":"pith_short_16","alias_value":"IPKTZBFLN6HT3POG","created_at":"2026-07-05T01:08:10.905581+00:00"},{"alias_kind":"pith_short_8","alias_value":"IPKTZBFL","created_at":"2026-07-05T01:08:10.905581+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.00368","citing_title":"Neural Network-based Information-Theoretic Transceivers for High-Order Modulation Schemes","ref_index":2019,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/IPKTZBFLN6HT3POGQKTZQL5J3T","json":"https://pith.science/pith/IPKTZBFLN6HT3POGQKTZQL5J3T.json","graph_json":"https://pith.science/api/pith-number/IPKTZBFLN6HT3POGQKTZQL5J3T/graph.json","events_json":"https://pith.science/api/pith-number/IPKTZBFLN6HT3POGQKTZQL5J3T/events.json","paper":"https://pith.science/paper/IPKTZBFL"},"agent_actions":{"view_html":"https://pith.science/pith/IPKTZBFLN6HT3POGQKTZQL5J3T","download_json":"https://pith.science/pith/IPKTZBFLN6HT3POGQKTZQL5J3T.json","view_paper":"https://pith.science/paper/IPKTZBFL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1911.13055&json=true","fetch_graph":"https://pith.science/api/pith-number/IPKTZBFLN6HT3POGQKTZQL5J3T/graph.json","fetch_events":"https://pith.science/api/pith-number/IPKTZBFLN6HT3POGQKTZQL5J3T/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IPKTZBFLN6HT3POGQKTZQL5J3T/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IPKTZBFLN6HT3POGQKTZQL5J3T/action/storage_attestation","attest_author":"https://pith.science/pith/IPKTZBFLN6HT3POGQKTZQL5J3T/action/author_attestation","sign_citation":"https://pith.science/pith/IPKTZBFLN6HT3POGQKTZQL5J3T/action/citation_signature","submit_replication":"https://pith.science/pith/IPKTZBFLN6HT3POGQKTZQL5J3T/action/replication_record"}},"created_at":"2026-07-05T01:08:10.905581+00:00","updated_at":"2026-07-05T01:08:10.905581+00:00"}