{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:VCRWYM4VSWTXLKO5EG5UI4563B","short_pith_number":"pith:VCRWYM4V","schema_version":"1.0","canonical_sha256":"a8a36c339595a775a9dd21bb4473bed84888094ee2bb4af18dc2482857b71e6c","source":{"kind":"arxiv","id":"1707.03384","version":1},"attestation_state":"computed","paper":{"title":"Deep Learning-Based Communication Over the Air","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","math.IT"],"primary_cat":"stat.ML","authors_text":"Jakob Hoydis, Sebastian Cammerer, Sebastian D\\\"orner, Stephan ten Brink","submitted_at":"2017-07-11T17:47:23Z","abstract_excerpt":"End-to-end learning of communications systems is a fascinating novel concept that has so far only been validated by simulations for block-based transmissions. It allows learning of transmitter and receiver implementations as deep neural networks (NNs) that are optimized for an arbitrary differentiable end-to-end performance metric, e.g., block error rate (BLER). In this paper, we demonstrate that over-the-air transmissions are possible: We build, train, and run a complete communications system solely composed of NNs using unsynchronized off-the-shelf software-defined radios (SDRs) and open-sou"},"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":"1707.03384","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-07-11T17:47:23Z","cross_cats_sorted":["cs.IT","math.IT"],"title_canon_sha256":"0dc38d139cc24b53c6e76644389c96040f92c9258fa178c5b2f7fa092071ac38","abstract_canon_sha256":"56c84287393f04200952cc82d7b73a70ed0b5af6022afc6e3a30e06b72ffcf33"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:21:27.647864Z","signature_b64":"4pC+4hiD/pHFi3mIWlFBahG4Yl1guddStSuT3lJQQNv8k4qKvaTOngJY94QiPUVi/EQEsTnnvNmJpywF0+AYAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a8a36c339595a775a9dd21bb4473bed84888094ee2bb4af18dc2482857b71e6c","last_reissued_at":"2026-05-18T00:21:27.647285Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:21:27.647285Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Learning-Based Communication Over the Air","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","math.IT"],"primary_cat":"stat.ML","authors_text":"Jakob Hoydis, Sebastian Cammerer, Sebastian D\\\"orner, Stephan ten Brink","submitted_at":"2017-07-11T17:47:23Z","abstract_excerpt":"End-to-end learning of communications systems is a fascinating novel concept that has so far only been validated by simulations for block-based transmissions. It allows learning of transmitter and receiver implementations as deep neural networks (NNs) that are optimized for an arbitrary differentiable end-to-end performance metric, e.g., block error rate (BLER). In this paper, we demonstrate that over-the-air transmissions are possible: We build, train, and run a complete communications system solely composed of NNs using unsynchronized off-the-shelf software-defined radios (SDRs) and open-sou"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1707.03384","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":"1707.03384","created_at":"2026-05-18T00:21:27.647383+00:00"},{"alias_kind":"arxiv_version","alias_value":"1707.03384v1","created_at":"2026-05-18T00:21:27.647383+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1707.03384","created_at":"2026-05-18T00:21:27.647383+00:00"},{"alias_kind":"pith_short_12","alias_value":"VCRWYM4VSWTX","created_at":"2026-05-18T12:31:49.984773+00:00"},{"alias_kind":"pith_short_16","alias_value":"VCRWYM4VSWTXLKO5","created_at":"2026-05-18T12:31:49.984773+00:00"},{"alias_kind":"pith_short_8","alias_value":"VCRWYM4V","created_at":"2026-05-18T12:31:49.984773+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/VCRWYM4VSWTXLKO5EG5UI4563B","json":"https://pith.science/pith/VCRWYM4VSWTXLKO5EG5UI4563B.json","graph_json":"https://pith.science/api/pith-number/VCRWYM4VSWTXLKO5EG5UI4563B/graph.json","events_json":"https://pith.science/api/pith-number/VCRWYM4VSWTXLKO5EG5UI4563B/events.json","paper":"https://pith.science/paper/VCRWYM4V"},"agent_actions":{"view_html":"https://pith.science/pith/VCRWYM4VSWTXLKO5EG5UI4563B","download_json":"https://pith.science/pith/VCRWYM4VSWTXLKO5EG5UI4563B.json","view_paper":"https://pith.science/paper/VCRWYM4V","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1707.03384&json=true","fetch_graph":"https://pith.science/api/pith-number/VCRWYM4VSWTXLKO5EG5UI4563B/graph.json","fetch_events":"https://pith.science/api/pith-number/VCRWYM4VSWTXLKO5EG5UI4563B/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VCRWYM4VSWTXLKO5EG5UI4563B/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VCRWYM4VSWTXLKO5EG5UI4563B/action/storage_attestation","attest_author":"https://pith.science/pith/VCRWYM4VSWTXLKO5EG5UI4563B/action/author_attestation","sign_citation":"https://pith.science/pith/VCRWYM4VSWTXLKO5EG5UI4563B/action/citation_signature","submit_replication":"https://pith.science/pith/VCRWYM4VSWTXLKO5EG5UI4563B/action/replication_record"}},"created_at":"2026-05-18T00:21:27.647383+00:00","updated_at":"2026-05-18T00:21:27.647383+00:00"}