{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:2DA46RT3VQPI25VHTDUACEN2KY","short_pith_number":"pith:2DA46RT3","schema_version":"1.0","canonical_sha256":"d0c1cf467bac1e8d76a798e80111ba560da7208a3f5d0a85602555a64a06e917","source":{"kind":"arxiv","id":"1810.00774","version":1},"attestation_state":"computed","paper":{"title":"Geometric Constellation Shaping for Fiber Optic Communication Systems via End-to-end Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT","stat.ML"],"primary_cat":"cs.IT","authors_text":"Benjamin J. Puttnam, Darko Zibar, Georg Rademacher, Metodi P. Yankov, Rasmus T. Jones, Ruben S. Luis, Tobias A. Eriksson","submitted_at":"2018-10-01T15:53:19Z","abstract_excerpt":"In this paper, an unsupervised machine learning method for geometric constellation shaping is investigated. By embedding a differentiable fiber channel model within two neural networks, the learning algorithm is optimizing for a geometric constellation shape. The learned constellations yield improved performance to state-of-the-art geometrically shaped constellations, and include an implicit trade-off between amplification noise and nonlinear effects. Further, the method allows joint optimization of system parameters, such as the optimal launch power, simultaneously with the constellation shap"},"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":"1810.00774","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2018-10-01T15:53:19Z","cross_cats_sorted":["math.IT","stat.ML"],"title_canon_sha256":"b29632664c8a06904989f78079dc1d8c68899425ee44e57fd1a04f0660d95887","abstract_canon_sha256":"9384f73a21afda854696ba7e1342ae7e25b639f7d2c06d05c71ff744a0c2b822"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:04:24.877522Z","signature_b64":"+NmAr9SuERzWtk8Cw1Z6iGg2q8JTcOHRp1Gcmb9nS/7qv+u0EMYX583X0la7a/gAWb5U6ug9ZydP20ZEJKDZCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d0c1cf467bac1e8d76a798e80111ba560da7208a3f5d0a85602555a64a06e917","last_reissued_at":"2026-05-18T00:04:24.876977Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:04:24.876977Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Geometric Constellation Shaping for Fiber Optic Communication Systems via End-to-end Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT","stat.ML"],"primary_cat":"cs.IT","authors_text":"Benjamin J. Puttnam, Darko Zibar, Georg Rademacher, Metodi P. Yankov, Rasmus T. Jones, Ruben S. Luis, Tobias A. Eriksson","submitted_at":"2018-10-01T15:53:19Z","abstract_excerpt":"In this paper, an unsupervised machine learning method for geometric constellation shaping is investigated. By embedding a differentiable fiber channel model within two neural networks, the learning algorithm is optimizing for a geometric constellation shape. The learned constellations yield improved performance to state-of-the-art geometrically shaped constellations, and include an implicit trade-off between amplification noise and nonlinear effects. Further, the method allows joint optimization of system parameters, such as the optimal launch power, simultaneously with the constellation shap"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.00774","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":"1810.00774","created_at":"2026-05-18T00:04:24.877082+00:00"},{"alias_kind":"arxiv_version","alias_value":"1810.00774v1","created_at":"2026-05-18T00:04:24.877082+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.00774","created_at":"2026-05-18T00:04:24.877082+00:00"},{"alias_kind":"pith_short_12","alias_value":"2DA46RT3VQPI","created_at":"2026-05-18T12:31:59.375834+00:00"},{"alias_kind":"pith_short_16","alias_value":"2DA46RT3VQPI25VH","created_at":"2026-05-18T12:31:59.375834+00:00"},{"alias_kind":"pith_short_8","alias_value":"2DA46RT3","created_at":"2026-05-18T12:31:59.375834+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/2DA46RT3VQPI25VHTDUACEN2KY","json":"https://pith.science/pith/2DA46RT3VQPI25VHTDUACEN2KY.json","graph_json":"https://pith.science/api/pith-number/2DA46RT3VQPI25VHTDUACEN2KY/graph.json","events_json":"https://pith.science/api/pith-number/2DA46RT3VQPI25VHTDUACEN2KY/events.json","paper":"https://pith.science/paper/2DA46RT3"},"agent_actions":{"view_html":"https://pith.science/pith/2DA46RT3VQPI25VHTDUACEN2KY","download_json":"https://pith.science/pith/2DA46RT3VQPI25VHTDUACEN2KY.json","view_paper":"https://pith.science/paper/2DA46RT3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1810.00774&json=true","fetch_graph":"https://pith.science/api/pith-number/2DA46RT3VQPI25VHTDUACEN2KY/graph.json","fetch_events":"https://pith.science/api/pith-number/2DA46RT3VQPI25VHTDUACEN2KY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2DA46RT3VQPI25VHTDUACEN2KY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2DA46RT3VQPI25VHTDUACEN2KY/action/storage_attestation","attest_author":"https://pith.science/pith/2DA46RT3VQPI25VHTDUACEN2KY/action/author_attestation","sign_citation":"https://pith.science/pith/2DA46RT3VQPI25VHTDUACEN2KY/action/citation_signature","submit_replication":"https://pith.science/pith/2DA46RT3VQPI25VHTDUACEN2KY/action/replication_record"}},"created_at":"2026-05-18T00:04:24.877082+00:00","updated_at":"2026-05-18T00:04:24.877082+00:00"}