{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:YQMSNBKQL7QV7BJBGIWDJBJCHM","short_pith_number":"pith:YQMSNBKQ","schema_version":"1.0","canonical_sha256":"c4192685505fe15f8521322c3485223b355d342b090956c8d873ff32d672cefe","source":{"kind":"arxiv","id":"2001.00784","version":1},"attestation_state":"computed","paper":{"title":"Optimizing Wireless Systems Using Unsupervised and Reinforced-Unsupervised Deep Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Chengjian Sun, Chenyang Yang, Dong Liu, Lajos Hanzo","submitted_at":"2020-01-03T11:01:52Z","abstract_excerpt":"Resource allocation and transceivers in wireless networks are usually designed by solving optimization problems subject to specific constraints, which can be formulated as variable or functional optimization. If the objective and constraint functions of a variable optimization problem can be derived, standard numerical algorithms can be applied for finding the optimal solution, which however incur high computational cost when the dimension of the variable is high. To reduce the on-line computational complexity, learning the optimal solution as a function of the environment's status by deep neu"},"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":"2001.00784","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-01-03T11:01:52Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"f4af8bfdae9a92155f113e183603fba69e6bf22b05025fdc0f599c2d4438e38e","abstract_canon_sha256":"5bd02aa4c362d8e2958987733125c60eb2f821e9ffba0f43e4f2eea3b6f571ff"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:29:32.324172Z","signature_b64":"p3eZ3VnT9z4Rea3+Pk4SktENdFpoOtUjeXi/XsYh60nbBgdoV/HP4dQKlQiLbaeY4g8sgz3N7jT8NkqzVOBODw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c4192685505fe15f8521322c3485223b355d342b090956c8d873ff32d672cefe","last_reissued_at":"2026-07-05T00:29:32.323835Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:29:32.323835Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Optimizing Wireless Systems Using Unsupervised and Reinforced-Unsupervised Deep Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Chengjian Sun, Chenyang Yang, Dong Liu, Lajos Hanzo","submitted_at":"2020-01-03T11:01:52Z","abstract_excerpt":"Resource allocation and transceivers in wireless networks are usually designed by solving optimization problems subject to specific constraints, which can be formulated as variable or functional optimization. If the objective and constraint functions of a variable optimization problem can be derived, standard numerical algorithms can be applied for finding the optimal solution, which however incur high computational cost when the dimension of the variable is high. To reduce the on-line computational complexity, learning the optimal solution as a function of the environment's status by deep neu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2001.00784","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2001.00784/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":"2001.00784","created_at":"2026-07-05T00:29:32.323896+00:00"},{"alias_kind":"arxiv_version","alias_value":"2001.00784v1","created_at":"2026-07-05T00:29:32.323896+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2001.00784","created_at":"2026-07-05T00:29:32.323896+00:00"},{"alias_kind":"pith_short_12","alias_value":"YQMSNBKQL7QV","created_at":"2026-07-05T00:29:32.323896+00:00"},{"alias_kind":"pith_short_16","alias_value":"YQMSNBKQL7QV7BJB","created_at":"2026-07-05T00:29:32.323896+00:00"},{"alias_kind":"pith_short_8","alias_value":"YQMSNBKQ","created_at":"2026-07-05T00:29:32.323896+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/YQMSNBKQL7QV7BJBGIWDJBJCHM","json":"https://pith.science/pith/YQMSNBKQL7QV7BJBGIWDJBJCHM.json","graph_json":"https://pith.science/api/pith-number/YQMSNBKQL7QV7BJBGIWDJBJCHM/graph.json","events_json":"https://pith.science/api/pith-number/YQMSNBKQL7QV7BJBGIWDJBJCHM/events.json","paper":"https://pith.science/paper/YQMSNBKQ"},"agent_actions":{"view_html":"https://pith.science/pith/YQMSNBKQL7QV7BJBGIWDJBJCHM","download_json":"https://pith.science/pith/YQMSNBKQL7QV7BJBGIWDJBJCHM.json","view_paper":"https://pith.science/paper/YQMSNBKQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2001.00784&json=true","fetch_graph":"https://pith.science/api/pith-number/YQMSNBKQL7QV7BJBGIWDJBJCHM/graph.json","fetch_events":"https://pith.science/api/pith-number/YQMSNBKQL7QV7BJBGIWDJBJCHM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YQMSNBKQL7QV7BJBGIWDJBJCHM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YQMSNBKQL7QV7BJBGIWDJBJCHM/action/storage_attestation","attest_author":"https://pith.science/pith/YQMSNBKQL7QV7BJBGIWDJBJCHM/action/author_attestation","sign_citation":"https://pith.science/pith/YQMSNBKQL7QV7BJBGIWDJBJCHM/action/citation_signature","submit_replication":"https://pith.science/pith/YQMSNBKQL7QV7BJBGIWDJBJCHM/action/replication_record"}},"created_at":"2026-07-05T00:29:32.323896+00:00","updated_at":"2026-07-05T00:29:32.323896+00:00"}