{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2012:474B7WQBZBG5L4DHSQG4SNR4YR","short_pith_number":"pith:474B7WQB","schema_version":"1.0","canonical_sha256":"e7f81fda01c84dd5f067940dc9363cc477e0c91425bc6d9c29ec278701816aa7","source":{"kind":"arxiv","id":"1204.0656","version":1},"attestation_state":"computed","paper":{"title":"Application of Bayesian Hierarchical Prior Modeling to Sparse Channel Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ML","authors_text":"Bernard Henri Fleury, Carles Navarro Manch\\'on, Dmitriy Shutin, Niels Lovmand Pedersen","submitted_at":"2012-04-03T11:12:52Z","abstract_excerpt":"Existing methods for sparse channel estimation typically provide an estimate computed as the solution maximizing an objective function defined as the sum of the log-likelihood function and a penalization term proportional to the l1-norm of the parameter of interest. However, other penalization terms have proven to have strong sparsity-inducing properties. In this work, we design pilot-assisted channel estimators for OFDM wireless receivers within the framework of sparse Bayesian learning by defining hierarchical Bayesian prior models that lead to sparsity-inducing penalization terms. The estim"},"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":"1204.0656","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2012-04-03T11:12:52Z","cross_cats_sorted":[],"title_canon_sha256":"a2d2ce9248cf456c408288721e7af5696a29759781887289cb20654f7ed148ef","abstract_canon_sha256":"9efcdfe45dec3d6828a0be2c81b272cb154e761916fec687867a8200f3c95b5b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:58:38.867966Z","signature_b64":"9Anid+euL9b857GhC9nMf04Uhmuj4QjF6yu/yqWo4oKUVS3+4zBq8YloEWfMDasasFSK7y3KXNUhh/oUjrOXBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e7f81fda01c84dd5f067940dc9363cc477e0c91425bc6d9c29ec278701816aa7","last_reissued_at":"2026-05-18T03:58:38.867299Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:58:38.867299Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Application of Bayesian Hierarchical Prior Modeling to Sparse Channel Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ML","authors_text":"Bernard Henri Fleury, Carles Navarro Manch\\'on, Dmitriy Shutin, Niels Lovmand Pedersen","submitted_at":"2012-04-03T11:12:52Z","abstract_excerpt":"Existing methods for sparse channel estimation typically provide an estimate computed as the solution maximizing an objective function defined as the sum of the log-likelihood function and a penalization term proportional to the l1-norm of the parameter of interest. However, other penalization terms have proven to have strong sparsity-inducing properties. In this work, we design pilot-assisted channel estimators for OFDM wireless receivers within the framework of sparse Bayesian learning by defining hierarchical Bayesian prior models that lead to sparsity-inducing penalization terms. The estim"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1204.0656","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":"1204.0656","created_at":"2026-05-18T03:58:38.867401+00:00"},{"alias_kind":"arxiv_version","alias_value":"1204.0656v1","created_at":"2026-05-18T03:58:38.867401+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1204.0656","created_at":"2026-05-18T03:58:38.867401+00:00"},{"alias_kind":"pith_short_12","alias_value":"474B7WQBZBG5","created_at":"2026-05-18T12:26:53.410803+00:00"},{"alias_kind":"pith_short_16","alias_value":"474B7WQBZBG5L4DH","created_at":"2026-05-18T12:26:53.410803+00:00"},{"alias_kind":"pith_short_8","alias_value":"474B7WQB","created_at":"2026-05-18T12:26:53.410803+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/474B7WQBZBG5L4DHSQG4SNR4YR","json":"https://pith.science/pith/474B7WQBZBG5L4DHSQG4SNR4YR.json","graph_json":"https://pith.science/api/pith-number/474B7WQBZBG5L4DHSQG4SNR4YR/graph.json","events_json":"https://pith.science/api/pith-number/474B7WQBZBG5L4DHSQG4SNR4YR/events.json","paper":"https://pith.science/paper/474B7WQB"},"agent_actions":{"view_html":"https://pith.science/pith/474B7WQBZBG5L4DHSQG4SNR4YR","download_json":"https://pith.science/pith/474B7WQBZBG5L4DHSQG4SNR4YR.json","view_paper":"https://pith.science/paper/474B7WQB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1204.0656&json=true","fetch_graph":"https://pith.science/api/pith-number/474B7WQBZBG5L4DHSQG4SNR4YR/graph.json","fetch_events":"https://pith.science/api/pith-number/474B7WQBZBG5L4DHSQG4SNR4YR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/474B7WQBZBG5L4DHSQG4SNR4YR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/474B7WQBZBG5L4DHSQG4SNR4YR/action/storage_attestation","attest_author":"https://pith.science/pith/474B7WQBZBG5L4DHSQG4SNR4YR/action/author_attestation","sign_citation":"https://pith.science/pith/474B7WQBZBG5L4DHSQG4SNR4YR/action/citation_signature","submit_replication":"https://pith.science/pith/474B7WQBZBG5L4DHSQG4SNR4YR/action/replication_record"}},"created_at":"2026-05-18T03:58:38.867401+00:00","updated_at":"2026-05-18T03:58:38.867401+00:00"}