{"as_of":"2026-08-05T09:33:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7cfc4735da067f325325d5994caccf25d79f849e4c04ae43794ea064b490288e","coverage":[{"denominator":21,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":21,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-25T22:22:05.995658Z","state":"measured"},{"denominator":21,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":21,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-05T06:32:48.257954+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2606.24727/citation-record","integrity":"/paper/2606.24727/integrity","json":"/paper/2606.24727/citation-record.json","paper":"/paper/2606.24727"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-25T22:22:05.995658Z","title":"Layered space-time architecture for wi reless communi- cation in a fading environment when using multi-element ant ennas,","venue":null,"work_id":null,"year":1996},"citing_paper":{"arxiv_id":"2606.24727","last_updated":"2026-06-23T15:50:34Z","snapshot_observed_at":"2026-07-31T20:30:41.135165Z","submitted_at":"2026-06-23T15:50:34Z","title":"Time-varying Wireless Channel Tracking with Online Parameter Learning via the Birth-Death-Drift Model","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-25T22:22:05.995658Z"},"links":{"citing_paper":"/paper/2606.24727"},"observation_digest":"sha256:061f2ed16669a9303ff876cdc1d0c31117fdb90ab7874aa25cec3c07d6b73b5d","observation_id":"9a27698b-21a2-40d0-adfd-fe02f8bbcf3f","resolution":{"observed_at":"2026-06-25T22:22:05.995658Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-25T22:22:05.995658Z","title":"Linear transmit processing in mimo communications systems,","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2606.24727","last_updated":"2026-06-23T15:50:34Z","snapshot_observed_at":"2026-07-31T20:30:41.135165Z","submitted_at":"2026-06-23T15:50:34Z","title":"Time-varying Wireless Channel Tracking with Online Parameter Learning via the Birth-Death-Drift Model","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-25T22:22:05.995658Z"},"links":{"citing_paper":"/paper/2606.24727"},"observation_digest":"sha256:6717b1489b3c95af67394d6fdaf23ab4245a60d8e8f5c6856ebc46bdb93fac4b","observation_id":"8f6b032c-23c7-4cc8-8603-404922da4451","resolution":{"observed_at":"2026-06-25T22:22:05.995658Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-25T22:22:05.995658Z","title":"Energy and sp ectral efﬁciency of very large multiuser mimo systems,","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2606.24727","last_updated":"2026-06-23T15:50:34Z","snapshot_observed_at":"2026-07-31T20:30:41.135165Z","submitted_at":"2026-06-23T15:50:34Z","title":"Time-varying Wireless Channel Tracking with Online Parameter Learning via the Birth-Death-Drift Model","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-25T22:22:05.995658Z"},"links":{"citing_paper":"/paper/2606.24727"},"observation_digest":"sha256:2cca158fa497c21f2f31852dee7e662b20c5885fc9662d4c9edde9e010fa6cd0","observation_id":"8c494809-7e8b-47d0-a889-b55e39e93d0e","resolution":{"observed_at":"2026-06-25T22:22:05.995658Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-25T22:22:05.995658Z","title":"Channel estimation and prediction for 5g applications,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.24727","last_updated":"2026-06-23T15:50:34Z","snapshot_observed_at":"2026-07-31T20:30:41.135165Z","submitted_at":"2026-06-23T15:50:34Z","title":"Time-varying Wireless Channel Tracking with Online Parameter Learning via the Birth-Death-Drift Model","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-25T22:22:05.995658Z"},"links":{"citing_paper":"/paper/2606.24727"},"observation_digest":"sha256:6197465f4da346fb7f6ddfdf8e6ae482908ddec8e0f7ec58d772fc42e582b9f2","observation_id":"798825e4-9b7c-4406-90be-84948bd37112","resolution":{"observed_at":"2026-06-25T22:22:05.995658Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-25T22:22:05.995658Z","title":"Massive mimo networks: Spectral, energy, and hardware efﬁciency,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2606.24727","last_updated":"2026-06-23T15:50:34Z","snapshot_observed_at":"2026-07-31T20:30:41.135165Z","submitted_at":"2026-06-23T15:50:34Z","title":"Time-varying Wireless Channel Tracking with Online Parameter Learning via the Birth-Death-Drift Model","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-25T22:22:05.995658Z"},"links":{"citing_paper":"/paper/2606.24727"},"observation_digest":"sha256:17dbadb95b8058ea42442d447d21d96d1973fc94659075f960f41012614ce976","observation_id":"40c4481d-7733-4b84-bbb7-46b81979e0f9","resolution":{"observed_at":"2026-06-25T22:22:05.995658Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-25T22:22:05.995658Z","title":"Ch annel estimation for massive mimo using gaussian-mixture bayesi an learning,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2606.24727","last_updated":"2026-06-23T15:50:34Z","snapshot_observed_at":"2026-07-31T20:30:41.135165Z","submitted_at":"2026-06-23T15:50:34Z","title":"Time-varying Wireless Channel Tracking with Online Parameter Learning via the Birth-Death-Drift Model","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-25T22:22:05.995658Z"},"links":{"citing_paper":"/paper/2606.24727"},"observation_digest":"sha256:85090235842bb69c4185332b4efaf08e12f53f32214cc981bc5266dabd8dd916","observation_id":"922937e5-2513-4c91-9741-74f9e31fbab5","resolution":{"observed_at":"2026-06-25T22:22:05.995658Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-25T22:22:05.995658Z","title":"A new approach to linear ﬁltering and predi ction problems,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2606.24727","last_updated":"2026-06-23T15:50:34Z","snapshot_observed_at":"2026-07-31T20:30:41.135165Z","submitted_at":"2026-06-23T15:50:34Z","title":"Time-varying Wireless Channel Tracking with Online Parameter Learning via the Birth-Death-Drift Model","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-25T22:22:05.995658Z"},"links":{"citing_paper":"/paper/2606.24727"},"observation_digest":"sha256:d20a801e1d4923ca871ec56ab93d7498c26e6a36a69d4e71b8caeea4e702d266","observation_id":"bdb278a1-dcdc-455f-b5f0-9faf030a0098","resolution":{"observed_at":"2026-06-25T22:22:05.995658Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1115/1.3","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T02:10:22.250823Z","title":"Available: https://doi.org/10.1115/1.3 662552","venue":null,"work_id":"15265479-9ce1-49f1-a56d-f0520e5e502a","year":null},"citing_paper":{"arxiv_id":"2606.24727","last_updated":"2026-06-23T15:50:34Z","snapshot_observed_at":"2026-07-31T20:30:41.135165Z","submitted_at":"2026-06-23T15:50:34Z","title":"Time-varying Wireless Channel Tracking with Online Parameter Learning via the Birth-Death-Drift Model","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-25T22:22:05.995658Z"},"links":{"citing_paper":"/paper/2606.24727"},"observation_digest":"sha256:c39b552386065a4888b72edd14c3c74432c0f4e1642175e975a9602f1c6fe261","observation_id":"bf958431-084a-43f1-8d21-e422f6087402","resolution":{"observed_at":"2026-06-27T02:10:22.251977Z","resolver_source":"doi_truncated","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-25T22:22:05.995658Z","title":"Adaptive ﬁlter theory,","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2606.24727","last_updated":"2026-06-23T15:50:34Z","snapshot_observed_at":"2026-07-31T20:30:41.135165Z","submitted_at":"2026-06-23T15:50:34Z","title":"Time-varying Wireless Channel Tracking with Online Parameter Learning via the Birth-Death-Drift Model","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-25T22:22:05.995658Z"},"links":{"citing_paper":"/paper/2606.24727"},"observation_digest":"sha256:fb9aaa3228b96353b05be8521ac90b213fac48f4dc3b81a4e93ee7c0e4807ec5","observation_id":"6f82bb6e-6217-4195-950f-c9ea0499b144","resolution":{"observed_at":"2026-06-25T22:22:05.995658Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-25T22:22:05.995658Z","title":null,"venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2606.24727","last_updated":"2026-06-23T15:50:34Z","snapshot_observed_at":"2026-07-31T20:30:41.135165Z","submitted_at":"2026-06-23T15:50:34Z","title":"Time-varying Wireless Channel Tracking with Online Parameter Learning via the Birth-Death-Drift Model","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-25T22:22:05.995658Z"},"links":{"citing_paper":"/paper/2606.24727"},"observation_digest":"sha256:4c1c861511f5a716fc78b4218b049bb37cc482cfcede886d37d6c7775ccf1705","observation_id":"bf1b125e-abfc-421a-9575-1a4877dbef65","resolution":{"observed_at":"2026-06-25T22:22:05.995658Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-25T22:22:05.995658Z","title":"Message-pas sing algo- rithms for compressed sensing,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2606.24727","last_updated":"2026-06-23T15:50:34Z","snapshot_observed_at":"2026-07-31T20:30:41.135165Z","submitted_at":"2026-06-23T15:50:34Z","title":"Time-varying Wireless Channel Tracking with Online Parameter Learning via the Birth-Death-Drift Model","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-25T22:22:05.995658Z"},"links":{"citing_paper":"/paper/2606.24727"},"observation_digest":"sha256:c6d2b7434a73a66a6db54e461282c3de7ec5de0907e3d7eea091923fd4721089","observation_id":"072befb2-4437-4583-97d6-78635b566d54","resolution":{"observed_at":"2026-06-25T22:22:05.995658Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-25T22:22:05.995658Z","title":"An appr oximate message passing framework for side information,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.24727","last_updated":"2026-06-23T15:50:34Z","snapshot_observed_at":"2026-07-31T20:30:41.135165Z","submitted_at":"2026-06-23T15:50:34Z","title":"Time-varying Wireless Channel Tracking with Online Parameter Learning via the Birth-Death-Drift Model","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-25T22:22:05.995658Z"},"links":{"citing_paper":"/paper/2606.24727"},"observation_digest":"sha256:b00b83daa4d37ba11c75878fcc2dd92ac145fbe5e75394e7b46012569f7f036f","observation_id":"fb63ac72-4016-4fba-8f55-76bcf9c0d60e","resolution":{"observed_at":"2026-06-25T22:22:05.995658Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-25T22:22:05.995658Z","title":"State evolution for gen eral approximate message passing algorithms, with applications to spatial c oupling,","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2606.24727","last_updated":"2026-06-23T15:50:34Z","snapshot_observed_at":"2026-07-31T20:30:41.135165Z","submitted_at":"2026-06-23T15:50:34Z","title":"Time-varying Wireless Channel Tracking with Online Parameter Learning via the Birth-Death-Drift Model","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-06-25T22:22:05.995658Z"},"links":{"citing_paper":"/paper/2606.24727"},"observation_digest":"sha256:06cbd01fa3ee18e7fe0d92a2c61f23c436e171020c2d4d3259e0bb4fbff3323e","observation_id":"8da28ba3-e8b4-4fbb-a493-9e0acf021e8f","resolution":{"observed_at":"2026-06-25T22:22:05.995658Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-25T22:22:05.995658Z","title":"Compressive sensing- based adaptive active user detection and channel estimatio n: Massive access meets massive mimo,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.24727","last_updated":"2026-06-23T15:50:34Z","snapshot_observed_at":"2026-07-31T20:30:41.135165Z","submitted_at":"2026-06-23T15:50:34Z","title":"Time-varying Wireless Channel Tracking with Online Parameter Learning via the Birth-Death-Drift Model","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-06-25T22:22:05.995658Z"},"links":{"citing_paper":"/paper/2606.24727"},"observation_digest":"sha256:38d2f2e0bddcb51a1c5865cbdc68bf75c31672dd59f13768f083d1f1302bedca","observation_id":"9237520b-b340-4dee-8d5c-d982c6a1bd8e","resolution":{"observed_at":"2026-06-25T22:22:05.995658Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-25T22:22:05.995658Z","title":"Expectation-maximization gaussian-mixture approximate message passing,","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2606.24727","last_updated":"2026-06-23T15:50:34Z","snapshot_observed_at":"2026-07-31T20:30:41.135165Z","submitted_at":"2026-06-23T15:50:34Z","title":"Time-varying Wireless Channel Tracking with Online Parameter Learning via the Birth-Death-Drift Model","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-25T22:22:05.995658Z"},"links":{"citing_paper":"/paper/2606.24727"},"observation_digest":"sha256:0dd834239db8fe2fbb0954a6c94f2363b81cb7c0feae0ac3f786ccf12ab21a3e","observation_id":"e312f1cd-4d2e-4d10-bbd8-61d11799b5dd","resolution":{"observed_at":"2026-06-25T22:22:05.995658Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-25T22:22:05.995658Z","title":"V ector appr oximate message passing,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.24727","last_updated":"2026-06-23T15:50:34Z","snapshot_observed_at":"2026-07-31T20:30:41.135165Z","submitted_at":"2026-06-23T15:50:34Z","title":"Time-varying Wireless Channel Tracking with Online Parameter Learning via the Birth-Death-Drift Model","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-06-25T22:22:05.995658Z"},"links":{"citing_paper":"/paper/2606.24727"},"observation_digest":"sha256:25650156c108b95829bafc436909aa27d0fdd72f208db23689ba4e4744ce9577","observation_id":"f3b9f6f1-edbb-46b6-aab7-5fba609795af","resolution":{"observed_at":"2026-06-25T22:22:05.995658Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-25T22:22:05.995658Z","title":null,"venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2606.24727","last_updated":"2026-06-23T15:50:34Z","snapshot_observed_at":"2026-07-31T20:30:41.135165Z","submitted_at":"2026-06-23T15:50:34Z","title":"Time-varying Wireless Channel Tracking with Online Parameter Learning via the Birth-Death-Drift Model","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-06-25T22:22:05.995658Z"},"links":{"citing_paper":"/paper/2606.24727"},"observation_digest":"sha256:31e6bc8f5258f20788e151350d127f0bd71499c10bc085e8c9a38a76bfe5bd38","observation_id":"e3d8fabb-bfed-4586-94b2-0546677e516d","resolution":{"observed_at":"2026-06-25T22:22:05.995658Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-25T22:22:05.995658Z","title":"Deconstructing multiantenna fading cha nnels,","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2606.24727","last_updated":"2026-06-23T15:50:34Z","snapshot_observed_at":"2026-07-31T20:30:41.135165Z","submitted_at":"2026-06-23T15:50:34Z","title":"Time-varying Wireless Channel Tracking with Online Parameter Learning via the Birth-Death-Drift Model","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-06-25T22:22:05.995658Z"},"links":{"citing_paper":"/paper/2606.24727"},"observation_digest":"sha256:6bd1861fdeb26df58574f19e94f2501e354a23dbb43bea80b55fc325e3ed7d3f","observation_id":"ff211f83-f6e0-4362-8a7b-aa304d7cab82","resolution":{"observed_at":"2026-06-25T22:22:05.995658Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-25T22:22:05.995658Z","title":"Sparse bayesian learning and the releva nce vector machine,","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2606.24727","last_updated":"2026-06-23T15:50:34Z","snapshot_observed_at":"2026-07-31T20:30:41.135165Z","submitted_at":"2026-06-23T15:50:34Z","title":"Time-varying Wireless Channel Tracking with Online Parameter Learning via the Birth-Death-Drift Model","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-06-25T22:22:05.995658Z"},"links":{"citing_paper":"/paper/2606.24727"},"observation_digest":"sha256:579c9c773f5d754e92f7aeedc8cd2f2649a6f3ff156193a52987d1ddbcf64ccb","observation_id":"2a453c18-9bb2-4bc6-97f1-912ff82f3b05","resolution":{"observed_at":"2026-06-25T22:22:05.995658Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-25T22:22:05.995658Z","title":"Learning and free energ ies for vector approximate message passing,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2606.24727","last_updated":"2026-06-23T15:50:34Z","snapshot_observed_at":"2026-07-31T20:30:41.135165Z","submitted_at":"2026-06-23T15:50:34Z","title":"Time-varying Wireless Channel Tracking with Online Parameter Learning via the Birth-Death-Drift Model","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-06-25T22:22:05.995658Z"},"links":{"citing_paper":"/paper/2606.24727"},"observation_digest":"sha256:60870fa7aaf19ac2b9ba40f796a727b8dbcd4cfb9549e694b74c0a225a312bad","observation_id":"2ebe16e4-a4e7-4c26-8a68-50b14cf0df62","resolution":{"observed_at":"2026-06-25T22:22:05.995658Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-25T22:22:05.995658Z","title":"Parameter estimation f or linear dynamical systems,","venue":null,"work_id":null,"year":1996},"citing_paper":{"arxiv_id":"2606.24727","last_updated":"2026-06-23T15:50:34Z","snapshot_observed_at":"2026-07-31T20:30:41.135165Z","submitted_at":"2026-06-23T15:50:34Z","title":"Time-varying Wireless Channel Tracking with Online Parameter Learning via the Birth-Death-Drift Model","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-06-25T22:22:05.995658Z"},"links":{"citing_paper":"/paper/2606.24727"},"observation_digest":"sha256:39e1683e364ee333fbca4afa3400a381ddd596298be784dbedeb32dd67b3ff51","observation_id":"82aa950c-4f36-49da-9410-f40c573e6dc8","resolution":{"observed_at":"2026-06-25T22:22:05.995658Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2606.24727","last_updated":"2026-06-23T15:50:34Z","latest_version":1,"primary_category":"cs.IT","snapshot_observed_at":"2026-07-31T20:30:41.135165Z","submitted_at":"2026-06-23T15:50:34Z","title":"Time-varying Wireless Channel Tracking with Online Parameter Learning via the Birth-Death-Drift Model"},"reference_resolution":{"displayed":21,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":20,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":21},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"thesis":"As of 5 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2606.24727."}