{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:W2SNK3EHLGLIURKAXLS7ZZA5RJ","short_pith_number":"pith:W2SNK3EH","schema_version":"1.0","canonical_sha256":"b6a4d56c8759968a4540bae5fce41d8a65661aab9a60d8d76a410f5b4b9adbdd","source":{"kind":"arxiv","id":"2206.15095","version":1},"attestation_state":"computed","paper":{"title":"Learning-Aided Beam Prediction in mmWave MU-MIMO Systems for High-Speed Railway","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.SP","math.IT"],"primary_cat":"cs.IT","authors_text":"Fan Meng, Shengheng Liu, Yongming Huang, Zhaohua Lu","submitted_at":"2022-06-30T07:54:57Z","abstract_excerpt":"The problem of beam alignment and tracking in high mobility scenarios such as high-speed railway (HSR) becomes extremely challenging, since large overhead cost and significant time delay are introduced for fast time-varying channel estimation. To tackle this challenge, we propose a learning-aided beam prediction scheme for HSR networks, which predicts the beam directions and the channel amplitudes within a period of future time with fine time granularity, using a group of observations. Concretely, we transform the problem of high-dimensional beam prediction into a two-stage task, i.e., a low-d"},"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":"2206.15095","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2022-06-30T07:54:57Z","cross_cats_sorted":["eess.SP","math.IT"],"title_canon_sha256":"6bcac70971911b0a097adad35e1112de75067ec26dfe9673f11bbd74f9f0252b","abstract_canon_sha256":"6e441ea970cdf0af10cfa3125e6f029ee912a85a43405b2a79f1d93ec129b732"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:36:25.347760Z","signature_b64":"nVja5rBZ9dnJB2gQx87LrW67dx40oJsya+Jaz/cBLgEyWG7IJfBwPYGWxKJZvQvjWpuk20SK8UnZAJlk9KuCAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b6a4d56c8759968a4540bae5fce41d8a65661aab9a60d8d76a410f5b4b9adbdd","last_reissued_at":"2026-07-05T04:36:25.347308Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:36:25.347308Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning-Aided Beam Prediction in mmWave MU-MIMO Systems for High-Speed Railway","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.SP","math.IT"],"primary_cat":"cs.IT","authors_text":"Fan Meng, Shengheng Liu, Yongming Huang, Zhaohua Lu","submitted_at":"2022-06-30T07:54:57Z","abstract_excerpt":"The problem of beam alignment and tracking in high mobility scenarios such as high-speed railway (HSR) becomes extremely challenging, since large overhead cost and significant time delay are introduced for fast time-varying channel estimation. To tackle this challenge, we propose a learning-aided beam prediction scheme for HSR networks, which predicts the beam directions and the channel amplitudes within a period of future time with fine time granularity, using a group of observations. Concretely, we transform the problem of high-dimensional beam prediction into a two-stage task, i.e., a low-d"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.15095","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/2206.15095/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":"2206.15095","created_at":"2026-07-05T04:36:25.347380+00:00"},{"alias_kind":"arxiv_version","alias_value":"2206.15095v1","created_at":"2026-07-05T04:36:25.347380+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.15095","created_at":"2026-07-05T04:36:25.347380+00:00"},{"alias_kind":"pith_short_12","alias_value":"W2SNK3EHLGLI","created_at":"2026-07-05T04:36:25.347380+00:00"},{"alias_kind":"pith_short_16","alias_value":"W2SNK3EHLGLIURKA","created_at":"2026-07-05T04:36:25.347380+00:00"},{"alias_kind":"pith_short_8","alias_value":"W2SNK3EH","created_at":"2026-07-05T04:36:25.347380+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/W2SNK3EHLGLIURKAXLS7ZZA5RJ","json":"https://pith.science/pith/W2SNK3EHLGLIURKAXLS7ZZA5RJ.json","graph_json":"https://pith.science/api/pith-number/W2SNK3EHLGLIURKAXLS7ZZA5RJ/graph.json","events_json":"https://pith.science/api/pith-number/W2SNK3EHLGLIURKAXLS7ZZA5RJ/events.json","paper":"https://pith.science/paper/W2SNK3EH"},"agent_actions":{"view_html":"https://pith.science/pith/W2SNK3EHLGLIURKAXLS7ZZA5RJ","download_json":"https://pith.science/pith/W2SNK3EHLGLIURKAXLS7ZZA5RJ.json","view_paper":"https://pith.science/paper/W2SNK3EH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2206.15095&json=true","fetch_graph":"https://pith.science/api/pith-number/W2SNK3EHLGLIURKAXLS7ZZA5RJ/graph.json","fetch_events":"https://pith.science/api/pith-number/W2SNK3EHLGLIURKAXLS7ZZA5RJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/W2SNK3EHLGLIURKAXLS7ZZA5RJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/W2SNK3EHLGLIURKAXLS7ZZA5RJ/action/storage_attestation","attest_author":"https://pith.science/pith/W2SNK3EHLGLIURKAXLS7ZZA5RJ/action/author_attestation","sign_citation":"https://pith.science/pith/W2SNK3EHLGLIURKAXLS7ZZA5RJ/action/citation_signature","submit_replication":"https://pith.science/pith/W2SNK3EHLGLIURKAXLS7ZZA5RJ/action/replication_record"}},"created_at":"2026-07-05T04:36:25.347380+00:00","updated_at":"2026-07-05T04:36:25.347380+00:00"}