{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:YCGO427RAVVGSXUPUUPWT2WAU5","short_pith_number":"pith:YCGO427R","schema_version":"1.0","canonical_sha256":"c08cee6bf1056a695e8fa51f69eac0a7751abd1f710703c7928009844151a783","source":{"kind":"arxiv","id":"2009.09967","version":1},"attestation_state":"computed","paper":{"title":"Massive MIMO Channel Prediction: Kalman Filtering vs. Machine Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.IT"],"primary_cat":"cs.IT","authors_text":"Chulhee Jang, Hwanjin Kim, Hyeongtaek Lee, Junil Choi, Sucheol Kim, Yongyun Choi","submitted_at":"2020-09-21T15:47:34Z","abstract_excerpt":"This paper focuses on channel prediction techniques for massive multiple-input multiple-output (MIMO) systems. Previous channel predictors are based on theoretical channel models, which would be deviated from realistic channels. In this paper, we develop and compare a vector Kalman filter (VKF)-based channel predictor and a machine learning (ML)-based channel predictor using the realistic channels from the spatial channel model (SCM), which has been adopted in the 3GPP standard for years. First, we propose a low-complexity mobility estimator based on the spatial average using a large number of"},"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":"2009.09967","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2020-09-21T15:47:34Z","cross_cats_sorted":["cs.LG","math.IT"],"title_canon_sha256":"f6240e7c13c65dceb077ceec0f599ee407ed0adc8106a2d55ec2d0e8dc008475","abstract_canon_sha256":"ce56c2060dd24e2d43e984f11682c45a15bee48d7543c7c3952aa0231c767740"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:34:44.336037Z","signature_b64":"lmjHBkyz1R6tIxAdy0NtcyZGhCtu9GjwI5t4K7QLI3JJMfEzvHnNfdWanSIsirna47q4SW96GzXX0k8Tzk5jAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c08cee6bf1056a695e8fa51f69eac0a7751abd1f710703c7928009844151a783","last_reissued_at":"2026-07-05T04:34:44.335533Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:34:44.335533Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Massive MIMO Channel Prediction: Kalman Filtering vs. Machine Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.IT"],"primary_cat":"cs.IT","authors_text":"Chulhee Jang, Hwanjin Kim, Hyeongtaek Lee, Junil Choi, Sucheol Kim, Yongyun Choi","submitted_at":"2020-09-21T15:47:34Z","abstract_excerpt":"This paper focuses on channel prediction techniques for massive multiple-input multiple-output (MIMO) systems. Previous channel predictors are based on theoretical channel models, which would be deviated from realistic channels. In this paper, we develop and compare a vector Kalman filter (VKF)-based channel predictor and a machine learning (ML)-based channel predictor using the realistic channels from the spatial channel model (SCM), which has been adopted in the 3GPP standard for years. First, we propose a low-complexity mobility estimator based on the spatial average using a large number of"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2009.09967","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/2009.09967/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":"2009.09967","created_at":"2026-07-05T04:34:44.335596+00:00"},{"alias_kind":"arxiv_version","alias_value":"2009.09967v1","created_at":"2026-07-05T04:34:44.335596+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2009.09967","created_at":"2026-07-05T04:34:44.335596+00:00"},{"alias_kind":"pith_short_12","alias_value":"YCGO427RAVVG","created_at":"2026-07-05T04:34:44.335596+00:00"},{"alias_kind":"pith_short_16","alias_value":"YCGO427RAVVGSXUP","created_at":"2026-07-05T04:34:44.335596+00:00"},{"alias_kind":"pith_short_8","alias_value":"YCGO427R","created_at":"2026-07-05T04:34:44.335596+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2411.18277","citing_title":"Large Models Enabled Ubiquitous Wireless Sensing","ref_index":6,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/YCGO427RAVVGSXUPUUPWT2WAU5","json":"https://pith.science/pith/YCGO427RAVVGSXUPUUPWT2WAU5.json","graph_json":"https://pith.science/api/pith-number/YCGO427RAVVGSXUPUUPWT2WAU5/graph.json","events_json":"https://pith.science/api/pith-number/YCGO427RAVVGSXUPUUPWT2WAU5/events.json","paper":"https://pith.science/paper/YCGO427R"},"agent_actions":{"view_html":"https://pith.science/pith/YCGO427RAVVGSXUPUUPWT2WAU5","download_json":"https://pith.science/pith/YCGO427RAVVGSXUPUUPWT2WAU5.json","view_paper":"https://pith.science/paper/YCGO427R","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2009.09967&json=true","fetch_graph":"https://pith.science/api/pith-number/YCGO427RAVVGSXUPUUPWT2WAU5/graph.json","fetch_events":"https://pith.science/api/pith-number/YCGO427RAVVGSXUPUUPWT2WAU5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YCGO427RAVVGSXUPUUPWT2WAU5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YCGO427RAVVGSXUPUUPWT2WAU5/action/storage_attestation","attest_author":"https://pith.science/pith/YCGO427RAVVGSXUPUUPWT2WAU5/action/author_attestation","sign_citation":"https://pith.science/pith/YCGO427RAVVGSXUPUUPWT2WAU5/action/citation_signature","submit_replication":"https://pith.science/pith/YCGO427RAVVGSXUPUUPWT2WAU5/action/replication_record"}},"created_at":"2026-07-05T04:34:44.335596+00:00","updated_at":"2026-07-05T04:34:44.335596+00:00"}