{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:FF276XRQ4BA25STH53LDBNVERC","short_pith_number":"pith:FF276XRQ","schema_version":"1.0","canonical_sha256":"2975ff5e30e041aeca67eed630b6a488ae850fefe4b83b4264f0729545b03f8d","source":{"kind":"arxiv","id":"2412.17414","version":1},"attestation_state":"computed","paper":{"title":"Spatio-Temporal Electromagnetic Kernel Learning for Channel Prediction","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["cs.IT","math.IT"],"primary_cat":"eess.SP","authors_text":"Jieao Zhu, Jinke Li, Linglong Dai","submitted_at":"2024-12-23T09:24:34Z","abstract_excerpt":"Accurate channel prediction is essential for addressing channel aging caused by user mobility. However, the actual channel variations over time are highly complex in high-mobility scenarios, which makes it difficult for existing predictors to obtain future channels accurately. The low accuracy of channel predictors leads to difficulties in supporting reliable communication. To overcome this challenge, we propose a channel predictor based on spatio-temporal electromagnetic (EM) kernel learning (STEM-KL). Specifically, inspired by recent advancements in EM information theory (EIT), the STEM kern"},"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":"2412.17414","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"eess.SP","submitted_at":"2024-12-23T09:24:34Z","cross_cats_sorted":["cs.IT","math.IT"],"title_canon_sha256":"ecca6cfc1ef8e8ed55d7d8e5104f602026c85538a897b6bd5dfae0b2cfb4e5ac","abstract_canon_sha256":"ae1b6134529f460892bb7b3883a0e87b1f590c808420243cff5b0822a473c6f5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:53:22.182196Z","signature_b64":"sW8E2AJL2kE+P5CTdxUmhE54YDQEWv8Oa8FQI8BVf+ioTJLlVQxEmUX0J2Fgpy0QDd9KUnkcRnRAu8uqu7tmCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2975ff5e30e041aeca67eed630b6a488ae850fefe4b83b4264f0729545b03f8d","last_reissued_at":"2026-07-05T09:53:22.181796Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:53:22.181796Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Spatio-Temporal Electromagnetic Kernel Learning for Channel Prediction","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["cs.IT","math.IT"],"primary_cat":"eess.SP","authors_text":"Jieao Zhu, Jinke Li, Linglong Dai","submitted_at":"2024-12-23T09:24:34Z","abstract_excerpt":"Accurate channel prediction is essential for addressing channel aging caused by user mobility. However, the actual channel variations over time are highly complex in high-mobility scenarios, which makes it difficult for existing predictors to obtain future channels accurately. The low accuracy of channel predictors leads to difficulties in supporting reliable communication. To overcome this challenge, we propose a channel predictor based on spatio-temporal electromagnetic (EM) kernel learning (STEM-KL). Specifically, inspired by recent advancements in EM information theory (EIT), the STEM kern"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.17414","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/2412.17414/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":"2412.17414","created_at":"2026-07-05T09:53:22.181854+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.17414v1","created_at":"2026-07-05T09:53:22.181854+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.17414","created_at":"2026-07-05T09:53:22.181854+00:00"},{"alias_kind":"pith_short_12","alias_value":"FF276XRQ4BA2","created_at":"2026-07-05T09:53:22.181854+00:00"},{"alias_kind":"pith_short_16","alias_value":"FF276XRQ4BA25STH","created_at":"2026-07-05T09:53:22.181854+00:00"},{"alias_kind":"pith_short_8","alias_value":"FF276XRQ","created_at":"2026-07-05T09:53:22.181854+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2512.22578","citing_title":"A Novel Geometry-Aware GPR-Based Energy-Efficient and Low-Overhead Channel Estimation Scheme","ref_index":22,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FF276XRQ4BA25STH53LDBNVERC","json":"https://pith.science/pith/FF276XRQ4BA25STH53LDBNVERC.json","graph_json":"https://pith.science/api/pith-number/FF276XRQ4BA25STH53LDBNVERC/graph.json","events_json":"https://pith.science/api/pith-number/FF276XRQ4BA25STH53LDBNVERC/events.json","paper":"https://pith.science/paper/FF276XRQ"},"agent_actions":{"view_html":"https://pith.science/pith/FF276XRQ4BA25STH53LDBNVERC","download_json":"https://pith.science/pith/FF276XRQ4BA25STH53LDBNVERC.json","view_paper":"https://pith.science/paper/FF276XRQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.17414&json=true","fetch_graph":"https://pith.science/api/pith-number/FF276XRQ4BA25STH53LDBNVERC/graph.json","fetch_events":"https://pith.science/api/pith-number/FF276XRQ4BA25STH53LDBNVERC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FF276XRQ4BA25STH53LDBNVERC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FF276XRQ4BA25STH53LDBNVERC/action/storage_attestation","attest_author":"https://pith.science/pith/FF276XRQ4BA25STH53LDBNVERC/action/author_attestation","sign_citation":"https://pith.science/pith/FF276XRQ4BA25STH53LDBNVERC/action/citation_signature","submit_replication":"https://pith.science/pith/FF276XRQ4BA25STH53LDBNVERC/action/replication_record"}},"created_at":"2026-07-05T09:53:22.181854+00:00","updated_at":"2026-07-05T09:53:22.181854+00:00"}