{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:KHWB32WDBD7JB244WTKKUBPQ3X","short_pith_number":"pith:KHWB32WD","schema_version":"1.0","canonical_sha256":"51ec1deac308fe90eb9cb4d4aa05f0ddc4ea166c66cc549c8771abc0d2996fc8","source":{"kind":"arxiv","id":"1905.03916","version":2},"attestation_state":"computed","paper":{"title":"Channel Covariance Matrix Estimation via Dimension Reduction for Hybrid MIMO MmWave Communication Systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Jiangtao Xi, Jun Tong, Qinghua Guo, Rui Hu, Yanguang Yu","submitted_at":"2019-05-10T02:54:04Z","abstract_excerpt":"Hybrid massive MIMO structures with lower hardware complexity and power consumption have been considered as a potential candidate for millimeter wave (mmWave) communications. Channel covariance information can be used for designing transmitter precoders, receiver combiners, channel estimators, etc. However, hybrid structures allow only a lower-dimensional signal to be observed, which adds difficulties for channel covariance matrix estimation. In this paper, we formulate the channel covariance estimation as a structured low-rank matrix sensing problem via Kronecker product expansion and use a l"},"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":"1905.03916","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2019-05-10T02:54:04Z","cross_cats_sorted":[],"title_canon_sha256":"638b68cc13d89a9430d3e9a2c9cb093c4a538dd4df4de98c99127cf97eacf4aa","abstract_canon_sha256":"a716ce5a67b0b457c732b0dff7b624d018319c5dff717dafe34d9b65033aed79"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:42:41.708541Z","signature_b64":"oPnFDY/zim+X1ryiSgQD32ZiRDYfaMM1tLlodNwr3PBpc+IbFCOl5Nwq7aer7l09kX+6weruaZJi7F3mBV1jCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"51ec1deac308fe90eb9cb4d4aa05f0ddc4ea166c66cc549c8771abc0d2996fc8","last_reissued_at":"2026-05-17T23:42:41.707973Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:42:41.707973Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Channel Covariance Matrix Estimation via Dimension Reduction for Hybrid MIMO MmWave Communication Systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Jiangtao Xi, Jun Tong, Qinghua Guo, Rui Hu, Yanguang Yu","submitted_at":"2019-05-10T02:54:04Z","abstract_excerpt":"Hybrid massive MIMO structures with lower hardware complexity and power consumption have been considered as a potential candidate for millimeter wave (mmWave) communications. Channel covariance information can be used for designing transmitter precoders, receiver combiners, channel estimators, etc. However, hybrid structures allow only a lower-dimensional signal to be observed, which adds difficulties for channel covariance matrix estimation. In this paper, we formulate the channel covariance estimation as a structured low-rank matrix sensing problem via Kronecker product expansion and use a l"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.03916","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"1905.03916","created_at":"2026-05-17T23:42:41.708041+00:00"},{"alias_kind":"arxiv_version","alias_value":"1905.03916v2","created_at":"2026-05-17T23:42:41.708041+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.03916","created_at":"2026-05-17T23:42:41.708041+00:00"},{"alias_kind":"pith_short_12","alias_value":"KHWB32WDBD7J","created_at":"2026-05-18T12:33:21.387695+00:00"},{"alias_kind":"pith_short_16","alias_value":"KHWB32WDBD7JB244","created_at":"2026-05-18T12:33:21.387695+00:00"},{"alias_kind":"pith_short_8","alias_value":"KHWB32WD","created_at":"2026-05-18T12:33:21.387695+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/KHWB32WDBD7JB244WTKKUBPQ3X","json":"https://pith.science/pith/KHWB32WDBD7JB244WTKKUBPQ3X.json","graph_json":"https://pith.science/api/pith-number/KHWB32WDBD7JB244WTKKUBPQ3X/graph.json","events_json":"https://pith.science/api/pith-number/KHWB32WDBD7JB244WTKKUBPQ3X/events.json","paper":"https://pith.science/paper/KHWB32WD"},"agent_actions":{"view_html":"https://pith.science/pith/KHWB32WDBD7JB244WTKKUBPQ3X","download_json":"https://pith.science/pith/KHWB32WDBD7JB244WTKKUBPQ3X.json","view_paper":"https://pith.science/paper/KHWB32WD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1905.03916&json=true","fetch_graph":"https://pith.science/api/pith-number/KHWB32WDBD7JB244WTKKUBPQ3X/graph.json","fetch_events":"https://pith.science/api/pith-number/KHWB32WDBD7JB244WTKKUBPQ3X/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KHWB32WDBD7JB244WTKKUBPQ3X/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KHWB32WDBD7JB244WTKKUBPQ3X/action/storage_attestation","attest_author":"https://pith.science/pith/KHWB32WDBD7JB244WTKKUBPQ3X/action/author_attestation","sign_citation":"https://pith.science/pith/KHWB32WDBD7JB244WTKKUBPQ3X/action/citation_signature","submit_replication":"https://pith.science/pith/KHWB32WDBD7JB244WTKKUBPQ3X/action/replication_record"}},"created_at":"2026-05-17T23:42:41.708041+00:00","updated_at":"2026-05-17T23:42:41.708041+00:00"}