{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:K7ISFW3Z4R46QHVVHP2CMDZT65","short_pith_number":"pith:K7ISFW3Z","schema_version":"1.0","canonical_sha256":"57d122db79e479e81eb53bf4260f33f7578002604709c90a57e2a3e65b772611","source":{"kind":"arxiv","id":"2408.06558","version":1},"attestation_state":"computed","paper":{"title":"Can Wireless Environmental Information Decrease Pilot Overhead: A CSI Prediction Example","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Guangyi Liu, Jianhua Zhang, Lianzheng Shi, Li Yu, Yichen Cai, Yuxiang Zhang, Zhen Zhang","submitted_at":"2024-08-13T01:44:05Z","abstract_excerpt":"Channel state information (CSI) is crucial for massive multi-input multi-output (MIMO) system. As the antenna scale increases, acquiring CSI results in significantly higher system overhead. In this letter, we propose a novel channel prediction method which utilizes wireless environmental information with pilot pattern optimization for CSI prediction (WEI-CSIP). Specifically, scatterers around the mobile station (MS) are abstracted from environmental information using multiview images. Then, an environmental feature map is extracted by a convolutional neural network (CNN). Additionally, the dee"},"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":"2408.06558","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2024-08-13T01:44:05Z","cross_cats_sorted":[],"title_canon_sha256":"bc246e42debdd1813057a32576e32d7c4a08b64db79ee2a10c36771342918ae9","abstract_canon_sha256":"d44c9c127d8de694f4a431888d59a1a88b84aeef578d42accbce1719f2166e82"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:54:58.660451Z","signature_b64":"AJU5NRoN1GuzmkDtoR3lsIJOJdXX5fBsLHIk6aNuh+kIYifdi0EKoeH17wkxZ1Ru54GX2SCK+0h2jOCaBwurAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"57d122db79e479e81eb53bf4260f33f7578002604709c90a57e2a3e65b772611","last_reissued_at":"2026-07-05T08:54:58.660066Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:54:58.660066Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Can Wireless Environmental Information Decrease Pilot Overhead: A CSI Prediction Example","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Guangyi Liu, Jianhua Zhang, Lianzheng Shi, Li Yu, Yichen Cai, Yuxiang Zhang, Zhen Zhang","submitted_at":"2024-08-13T01:44:05Z","abstract_excerpt":"Channel state information (CSI) is crucial for massive multi-input multi-output (MIMO) system. As the antenna scale increases, acquiring CSI results in significantly higher system overhead. In this letter, we propose a novel channel prediction method which utilizes wireless environmental information with pilot pattern optimization for CSI prediction (WEI-CSIP). Specifically, scatterers around the mobile station (MS) are abstracted from environmental information using multiview images. Then, an environmental feature map is extracted by a convolutional neural network (CNN). Additionally, the dee"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.06558","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/2408.06558/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":"2408.06558","created_at":"2026-07-05T08:54:58.660120+00:00"},{"alias_kind":"arxiv_version","alias_value":"2408.06558v1","created_at":"2026-07-05T08:54:58.660120+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.06558","created_at":"2026-07-05T08:54:58.660120+00:00"},{"alias_kind":"pith_short_12","alias_value":"K7ISFW3Z4R46","created_at":"2026-07-05T08:54:58.660120+00:00"},{"alias_kind":"pith_short_16","alias_value":"K7ISFW3Z4R46QHVV","created_at":"2026-07-05T08:54:58.660120+00:00"},{"alias_kind":"pith_short_8","alias_value":"K7ISFW3Z","created_at":"2026-07-05T08:54:58.660120+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.11479","citing_title":"Wireless Environmental Information Theory: A New Paradigm towards 6G Online and Proactive Environment Intelligence Communication","ref_index":31,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/K7ISFW3Z4R46QHVVHP2CMDZT65","json":"https://pith.science/pith/K7ISFW3Z4R46QHVVHP2CMDZT65.json","graph_json":"https://pith.science/api/pith-number/K7ISFW3Z4R46QHVVHP2CMDZT65/graph.json","events_json":"https://pith.science/api/pith-number/K7ISFW3Z4R46QHVVHP2CMDZT65/events.json","paper":"https://pith.science/paper/K7ISFW3Z"},"agent_actions":{"view_html":"https://pith.science/pith/K7ISFW3Z4R46QHVVHP2CMDZT65","download_json":"https://pith.science/pith/K7ISFW3Z4R46QHVVHP2CMDZT65.json","view_paper":"https://pith.science/paper/K7ISFW3Z","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2408.06558&json=true","fetch_graph":"https://pith.science/api/pith-number/K7ISFW3Z4R46QHVVHP2CMDZT65/graph.json","fetch_events":"https://pith.science/api/pith-number/K7ISFW3Z4R46QHVVHP2CMDZT65/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/K7ISFW3Z4R46QHVVHP2CMDZT65/action/timestamp_anchor","attest_storage":"https://pith.science/pith/K7ISFW3Z4R46QHVVHP2CMDZT65/action/storage_attestation","attest_author":"https://pith.science/pith/K7ISFW3Z4R46QHVVHP2CMDZT65/action/author_attestation","sign_citation":"https://pith.science/pith/K7ISFW3Z4R46QHVVHP2CMDZT65/action/citation_signature","submit_replication":"https://pith.science/pith/K7ISFW3Z4R46QHVVHP2CMDZT65/action/replication_record"}},"created_at":"2026-07-05T08:54:58.660120+00:00","updated_at":"2026-07-05T08:54:58.660120+00:00"}