{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:3CVEEKR6ZWZ7IAD6R7TNUX7ZWH","short_pith_number":"pith:3CVEEKR6","schema_version":"1.0","canonical_sha256":"d8aa422a3ecdb3f4007e8fe6da5ff9b1e8d6779446762abe7ef415108dc3939f","source":{"kind":"arxiv","id":"2307.08822","version":2},"attestation_state":"computed","paper":{"title":"A Meta-Learning Based Precoder Optimization Framework for Rate-Splitting Multiple Access","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","cs.LG","math.IT"],"primary_cat":"eess.SP","authors_text":"Bruno Clerckx, Rafael Cerna Loli","submitted_at":"2023-07-17T20:31:41Z","abstract_excerpt":"In this letter, we propose the use of a meta-learning based precoder optimization framework to directly optimize the Rate-Splitting Multiple Access (RSMA) precoders with partial Channel State Information at the Transmitter (CSIT). By exploiting the overfitting of the compact neural network to maximize the explicit Average Sum-Rate (ASR) expression, we effectively bypass the need for any other training data while minimizing the total running time. Numerical results reveal that the meta-learning based solution achieves similar ASR performance to conventional precoder optimization in medium-scale"},"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":"2307.08822","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2023-07-17T20:31:41Z","cross_cats_sorted":["cs.IT","cs.LG","math.IT"],"title_canon_sha256":"d9a880b1d9045ce1cf5a6e27a4f5f1b1820ff95f5a18892da6d1bf463ef1de40","abstract_canon_sha256":"c07748c2919be2cb3b542cd1e65cf5b26c2477b794ef159b3b97e0fb5bec98b5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:56:36.432951Z","signature_b64":"tIfaiEFDL3IJDIN2iE5llPkHMzlPXPCH2SdDNAlQl02moQkn2rsKgKyk6xhDVirT8N3k6bhupKvplB0B320IAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d8aa422a3ecdb3f4007e8fe6da5ff9b1e8d6779446762abe7ef415108dc3939f","last_reissued_at":"2026-07-05T06:56:36.432490Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:56:36.432490Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Meta-Learning Based Precoder Optimization Framework for Rate-Splitting Multiple Access","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","cs.LG","math.IT"],"primary_cat":"eess.SP","authors_text":"Bruno Clerckx, Rafael Cerna Loli","submitted_at":"2023-07-17T20:31:41Z","abstract_excerpt":"In this letter, we propose the use of a meta-learning based precoder optimization framework to directly optimize the Rate-Splitting Multiple Access (RSMA) precoders with partial Channel State Information at the Transmitter (CSIT). By exploiting the overfitting of the compact neural network to maximize the explicit Average Sum-Rate (ASR) expression, we effectively bypass the need for any other training data while minimizing the total running time. Numerical results reveal that the meta-learning based solution achieves similar ASR performance to conventional precoder optimization in medium-scale"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.08822","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/2307.08822/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":"2307.08822","created_at":"2026-07-05T06:56:36.432546+00:00"},{"alias_kind":"arxiv_version","alias_value":"2307.08822v2","created_at":"2026-07-05T06:56:36.432546+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.08822","created_at":"2026-07-05T06:56:36.432546+00:00"},{"alias_kind":"pith_short_12","alias_value":"3CVEEKR6ZWZ7","created_at":"2026-07-05T06:56:36.432546+00:00"},{"alias_kind":"pith_short_16","alias_value":"3CVEEKR6ZWZ7IAD6","created_at":"2026-07-05T06:56:36.432546+00:00"},{"alias_kind":"pith_short_8","alias_value":"3CVEEKR6","created_at":"2026-07-05T06:56:36.432546+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/3CVEEKR6ZWZ7IAD6R7TNUX7ZWH","json":"https://pith.science/pith/3CVEEKR6ZWZ7IAD6R7TNUX7ZWH.json","graph_json":"https://pith.science/api/pith-number/3CVEEKR6ZWZ7IAD6R7TNUX7ZWH/graph.json","events_json":"https://pith.science/api/pith-number/3CVEEKR6ZWZ7IAD6R7TNUX7ZWH/events.json","paper":"https://pith.science/paper/3CVEEKR6"},"agent_actions":{"view_html":"https://pith.science/pith/3CVEEKR6ZWZ7IAD6R7TNUX7ZWH","download_json":"https://pith.science/pith/3CVEEKR6ZWZ7IAD6R7TNUX7ZWH.json","view_paper":"https://pith.science/paper/3CVEEKR6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2307.08822&json=true","fetch_graph":"https://pith.science/api/pith-number/3CVEEKR6ZWZ7IAD6R7TNUX7ZWH/graph.json","fetch_events":"https://pith.science/api/pith-number/3CVEEKR6ZWZ7IAD6R7TNUX7ZWH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3CVEEKR6ZWZ7IAD6R7TNUX7ZWH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3CVEEKR6ZWZ7IAD6R7TNUX7ZWH/action/storage_attestation","attest_author":"https://pith.science/pith/3CVEEKR6ZWZ7IAD6R7TNUX7ZWH/action/author_attestation","sign_citation":"https://pith.science/pith/3CVEEKR6ZWZ7IAD6R7TNUX7ZWH/action/citation_signature","submit_replication":"https://pith.science/pith/3CVEEKR6ZWZ7IAD6R7TNUX7ZWH/action/replication_record"}},"created_at":"2026-07-05T06:56:36.432546+00:00","updated_at":"2026-07-05T06:56:36.432546+00:00"}