{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:IYOSU75C7MHNI6NG262YKLHTU3","short_pith_number":"pith:IYOSU75C","schema_version":"1.0","canonical_sha256":"461d2a7fa2fb0ed479a6d7b5852cf3a6f76bc5ccc0ac7850b82a9938113b4a6e","source":{"kind":"arxiv","id":"1808.02394","version":1},"attestation_state":"computed","paper":{"title":"Application of End-to-End Deep Learning in Wireless Communications Systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.SP","math.IT"],"primary_cat":"cs.IT","authors_text":"Minhoe Kim, Ohyun Jo, Woongsup Lee","submitted_at":"2018-08-07T14:20:10Z","abstract_excerpt":"Deep learning is a potential paradigm changer for the design of wireless communications systems (WCS), from conventional handcrafted schemes based on sophisticated mathematical models with assumptions to autonomous schemes based on the end-to-end deep learning using a large number of data. In this article, we present a basic concept of the deep learning and its application to WCS by investigating the resource allocation (RA) scheme based on a deep neural network (DNN) where multiple goals with various constraints can be satisfied through the end-to-end deep learning. Especially, the optimality"},"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":"1808.02394","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2018-08-07T14:20:10Z","cross_cats_sorted":["cs.LG","eess.SP","math.IT"],"title_canon_sha256":"1720fd7208c604e976c554b996089d36dd66c5e347a0fe0aacd01839ac113d37","abstract_canon_sha256":"5b0c94121c9a397664aa8d1ae7dccde8b3c23b6861be01393b4956f405c0eedf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:08:37.202641Z","signature_b64":"yGeXFyK7NFcr3gtxiNbw05bUwJmAP3+6qUzI4PAPILVVRp+Jfjn/rn1Sqe9xAg0MmAuHDYyV8/kDifHnneCsAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"461d2a7fa2fb0ed479a6d7b5852cf3a6f76bc5ccc0ac7850b82a9938113b4a6e","last_reissued_at":"2026-05-18T00:08:37.201961Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:08:37.201961Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Application of End-to-End Deep Learning in Wireless Communications Systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.SP","math.IT"],"primary_cat":"cs.IT","authors_text":"Minhoe Kim, Ohyun Jo, Woongsup Lee","submitted_at":"2018-08-07T14:20:10Z","abstract_excerpt":"Deep learning is a potential paradigm changer for the design of wireless communications systems (WCS), from conventional handcrafted schemes based on sophisticated mathematical models with assumptions to autonomous schemes based on the end-to-end deep learning using a large number of data. In this article, we present a basic concept of the deep learning and its application to WCS by investigating the resource allocation (RA) scheme based on a deep neural network (DNN) where multiple goals with various constraints can be satisfied through the end-to-end deep learning. Especially, the optimality"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.02394","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":"1808.02394","created_at":"2026-05-18T00:08:37.202077+00:00"},{"alias_kind":"arxiv_version","alias_value":"1808.02394v1","created_at":"2026-05-18T00:08:37.202077+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.02394","created_at":"2026-05-18T00:08:37.202077+00:00"},{"alias_kind":"pith_short_12","alias_value":"IYOSU75C7MHN","created_at":"2026-05-18T12:32:31.084164+00:00"},{"alias_kind":"pith_short_16","alias_value":"IYOSU75C7MHNI6NG","created_at":"2026-05-18T12:32:31.084164+00:00"},{"alias_kind":"pith_short_8","alias_value":"IYOSU75C","created_at":"2026-05-18T12:32:31.084164+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/IYOSU75C7MHNI6NG262YKLHTU3","json":"https://pith.science/pith/IYOSU75C7MHNI6NG262YKLHTU3.json","graph_json":"https://pith.science/api/pith-number/IYOSU75C7MHNI6NG262YKLHTU3/graph.json","events_json":"https://pith.science/api/pith-number/IYOSU75C7MHNI6NG262YKLHTU3/events.json","paper":"https://pith.science/paper/IYOSU75C"},"agent_actions":{"view_html":"https://pith.science/pith/IYOSU75C7MHNI6NG262YKLHTU3","download_json":"https://pith.science/pith/IYOSU75C7MHNI6NG262YKLHTU3.json","view_paper":"https://pith.science/paper/IYOSU75C","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1808.02394&json=true","fetch_graph":"https://pith.science/api/pith-number/IYOSU75C7MHNI6NG262YKLHTU3/graph.json","fetch_events":"https://pith.science/api/pith-number/IYOSU75C7MHNI6NG262YKLHTU3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IYOSU75C7MHNI6NG262YKLHTU3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IYOSU75C7MHNI6NG262YKLHTU3/action/storage_attestation","attest_author":"https://pith.science/pith/IYOSU75C7MHNI6NG262YKLHTU3/action/author_attestation","sign_citation":"https://pith.science/pith/IYOSU75C7MHNI6NG262YKLHTU3/action/citation_signature","submit_replication":"https://pith.science/pith/IYOSU75C7MHNI6NG262YKLHTU3/action/replication_record"}},"created_at":"2026-05-18T00:08:37.202077+00:00","updated_at":"2026-05-18T00:08:37.202077+00:00"}