{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:UCOHB62TLWQB4JYVCNVJXK6AFT","short_pith_number":"pith:UCOHB62T","schema_version":"1.0","canonical_sha256":"a09c70fb535da01e2715136a9babc02cd5cd4c20acfe7808022ef5f525fb24ea","source":{"kind":"arxiv","id":"2207.00934","version":1},"attestation_state":"computed","paper":{"title":"Wireless Channel Prediction in Partially Observed Environments","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","eess.SP"],"primary_cat":"cs.RO","authors_text":"Marco Mezzavilla, Mingsheng Yin, Seongjoon Kang, Sundeep Rangan, Tommy Azzino, Yaqi Hu","submitted_at":"2022-07-03T01:46:57Z","abstract_excerpt":"Site-specific radio frequency (RF) propagation prediction increasingly relies on models built from visual data such as cameras and LIDAR sensors. When operating in dynamic settings, the environment may only be partially observed. This paper introduces a method to extract statistical channel models, given partial observations of the surrounding environment. We propose a simple heuristic algorithm that performs ray tracing on the partial environment and then uses machine-learning trained predictors to estimate the channel and its uncertainty from features extracted from the partial ray tracing r"},"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":"2207.00934","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2022-07-03T01:46:57Z","cross_cats_sorted":["cs.LG","eess.SP"],"title_canon_sha256":"d1846f732039a0ce4e958598d82bb4495faaf643e05bf5d7971da15be0526067","abstract_canon_sha256":"695d2b252a33e5a8e4938885987a96f3e6e44d0e56611dc207c8c5cf43b0186a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:36:54.247189Z","signature_b64":"96uBE0dC2o3uwYCGAJcHbWGERjYkguFaVqR5WJOK7mex5l3CwNtTPteJtGX4n8LoylEYxCmInKSNywt+Tp/xBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a09c70fb535da01e2715136a9babc02cd5cd4c20acfe7808022ef5f525fb24ea","last_reissued_at":"2026-07-05T04:36:54.246761Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:36:54.246761Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Wireless Channel Prediction in Partially Observed Environments","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","eess.SP"],"primary_cat":"cs.RO","authors_text":"Marco Mezzavilla, Mingsheng Yin, Seongjoon Kang, Sundeep Rangan, Tommy Azzino, Yaqi Hu","submitted_at":"2022-07-03T01:46:57Z","abstract_excerpt":"Site-specific radio frequency (RF) propagation prediction increasingly relies on models built from visual data such as cameras and LIDAR sensors. When operating in dynamic settings, the environment may only be partially observed. This paper introduces a method to extract statistical channel models, given partial observations of the surrounding environment. We propose a simple heuristic algorithm that performs ray tracing on the partial environment and then uses machine-learning trained predictors to estimate the channel and its uncertainty from features extracted from the partial ray tracing r"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.00934","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/2207.00934/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":"2207.00934","created_at":"2026-07-05T04:36:54.246824+00:00"},{"alias_kind":"arxiv_version","alias_value":"2207.00934v1","created_at":"2026-07-05T04:36:54.246824+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.00934","created_at":"2026-07-05T04:36:54.246824+00:00"},{"alias_kind":"pith_short_12","alias_value":"UCOHB62TLWQB","created_at":"2026-07-05T04:36:54.246824+00:00"},{"alias_kind":"pith_short_16","alias_value":"UCOHB62TLWQB4JYV","created_at":"2026-07-05T04:36:54.246824+00:00"},{"alias_kind":"pith_short_8","alias_value":"UCOHB62T","created_at":"2026-07-05T04:36:54.246824+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/UCOHB62TLWQB4JYVCNVJXK6AFT","json":"https://pith.science/pith/UCOHB62TLWQB4JYVCNVJXK6AFT.json","graph_json":"https://pith.science/api/pith-number/UCOHB62TLWQB4JYVCNVJXK6AFT/graph.json","events_json":"https://pith.science/api/pith-number/UCOHB62TLWQB4JYVCNVJXK6AFT/events.json","paper":"https://pith.science/paper/UCOHB62T"},"agent_actions":{"view_html":"https://pith.science/pith/UCOHB62TLWQB4JYVCNVJXK6AFT","download_json":"https://pith.science/pith/UCOHB62TLWQB4JYVCNVJXK6AFT.json","view_paper":"https://pith.science/paper/UCOHB62T","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2207.00934&json=true","fetch_graph":"https://pith.science/api/pith-number/UCOHB62TLWQB4JYVCNVJXK6AFT/graph.json","fetch_events":"https://pith.science/api/pith-number/UCOHB62TLWQB4JYVCNVJXK6AFT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UCOHB62TLWQB4JYVCNVJXK6AFT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UCOHB62TLWQB4JYVCNVJXK6AFT/action/storage_attestation","attest_author":"https://pith.science/pith/UCOHB62TLWQB4JYVCNVJXK6AFT/action/author_attestation","sign_citation":"https://pith.science/pith/UCOHB62TLWQB4JYVCNVJXK6AFT/action/citation_signature","submit_replication":"https://pith.science/pith/UCOHB62TLWQB4JYVCNVJXK6AFT/action/replication_record"}},"created_at":"2026-07-05T04:36:54.246824+00:00","updated_at":"2026-07-05T04:36:54.246824+00:00"}