{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:PL67L4UGIUOI6HSM2PSV57UT4S","short_pith_number":"pith:PL67L4UG","schema_version":"1.0","canonical_sha256":"7afdf5f286451c8f1e4cd3e55efe93e4a4657396b416390d491f638c9b68baae","source":{"kind":"arxiv","id":"2101.10300","version":6},"attestation_state":"computed","paper":{"title":"Channel Estimation via Successive Denoising in MIMO OFDM Systems: A Reinforcement Learning Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"eess.SP","authors_text":"Christopher G. Brinton, David J. Love, Myeung Suk Oh, Seyyedali Hosseinalipour, Taejoon Kim","submitted_at":"2021-01-25T18:33:54Z","abstract_excerpt":"In general, reliable communication via multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) requires accurate channel estimation at the receiver. The existing literature largely focuses on denoising methods for channel estimation that depend on either (i) channel analysis in the time-domain with prior channel knowledge or (ii) supervised learning techniques which require large pre-labeled datasets for training. To address these limitations, we present a frequency-domain denoising method based on a reinforcement learning framework that does not need a priori c"},"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":"2101.10300","kind":"arxiv","version":6},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2021-01-25T18:33:54Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"92ed49b089d3d9491c7e20225795a37e8d7e51d1d7ef0bb600e3ef4caca62582","abstract_canon_sha256":"50de31bb20fee31de40a2b7cb340bc5f6e3c6a439d6d35986da539291335697e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-25T01:17:42.014184Z","signature_b64":"htIKl+PSm6d4h8ex5EVRGDSzks/j2UgblGu0YS9rj4HS/nqZ4lphJ1rHt/qRjY084sGTZ3+MskNgnVm1eXp4AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7afdf5f286451c8f1e4cd3e55efe93e4a4657396b416390d491f638c9b68baae","last_reissued_at":"2026-06-25T01:17:42.013678Z","signature_status":"signed_v1","first_computed_at":"2026-06-25T01:17:42.013678Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Channel Estimation via Successive Denoising in MIMO OFDM Systems: A Reinforcement Learning Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"eess.SP","authors_text":"Christopher G. Brinton, David J. Love, Myeung Suk Oh, Seyyedali Hosseinalipour, Taejoon Kim","submitted_at":"2021-01-25T18:33:54Z","abstract_excerpt":"In general, reliable communication via multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) requires accurate channel estimation at the receiver. The existing literature largely focuses on denoising methods for channel estimation that depend on either (i) channel analysis in the time-domain with prior channel knowledge or (ii) supervised learning techniques which require large pre-labeled datasets for training. To address these limitations, we present a frequency-domain denoising method based on a reinforcement learning framework that does not need a priori c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2101.10300","kind":"arxiv","version":6},"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/2101.10300/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":"2101.10300","created_at":"2026-06-25T01:17:42.013745+00:00"},{"alias_kind":"arxiv_version","alias_value":"2101.10300v6","created_at":"2026-06-25T01:17:42.013745+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2101.10300","created_at":"2026-06-25T01:17:42.013745+00:00"},{"alias_kind":"pith_short_12","alias_value":"PL67L4UGIUOI","created_at":"2026-06-25T01:17:42.013745+00:00"},{"alias_kind":"pith_short_16","alias_value":"PL67L4UGIUOI6HSM","created_at":"2026-06-25T01:17:42.013745+00:00"},{"alias_kind":"pith_short_8","alias_value":"PL67L4UG","created_at":"2026-06-25T01:17:42.013745+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/PL67L4UGIUOI6HSM2PSV57UT4S","json":"https://pith.science/pith/PL67L4UGIUOI6HSM2PSV57UT4S.json","graph_json":"https://pith.science/api/pith-number/PL67L4UGIUOI6HSM2PSV57UT4S/graph.json","events_json":"https://pith.science/api/pith-number/PL67L4UGIUOI6HSM2PSV57UT4S/events.json","paper":"https://pith.science/paper/PL67L4UG"},"agent_actions":{"view_html":"https://pith.science/pith/PL67L4UGIUOI6HSM2PSV57UT4S","download_json":"https://pith.science/pith/PL67L4UGIUOI6HSM2PSV57UT4S.json","view_paper":"https://pith.science/paper/PL67L4UG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2101.10300&json=true","fetch_graph":"https://pith.science/api/pith-number/PL67L4UGIUOI6HSM2PSV57UT4S/graph.json","fetch_events":"https://pith.science/api/pith-number/PL67L4UGIUOI6HSM2PSV57UT4S/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PL67L4UGIUOI6HSM2PSV57UT4S/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PL67L4UGIUOI6HSM2PSV57UT4S/action/storage_attestation","attest_author":"https://pith.science/pith/PL67L4UGIUOI6HSM2PSV57UT4S/action/author_attestation","sign_citation":"https://pith.science/pith/PL67L4UGIUOI6HSM2PSV57UT4S/action/citation_signature","submit_replication":"https://pith.science/pith/PL67L4UGIUOI6HSM2PSV57UT4S/action/replication_record"}},"created_at":"2026-06-25T01:17:42.013745+00:00","updated_at":"2026-06-25T01:17:42.013745+00:00"}