{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:UXX5FGWEUXVY6MRHFSBFTS5P3Q","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"c0d44eda245a80545552ba8e8d39a32a7f711e323cb160feadcf94ff27016625","cross_cats_sorted":["eess.SP","math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2024-02-01T21:44:57Z","title_canon_sha256":"8b3d7c2bb2551360f04e9dac84766cb0ef711fa9a490b78a7ec2a9f15bcc2df2"},"schema_version":"1.0","source":{"id":"2402.01033","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.01033","created_at":"2026-07-05T07:40:29Z"},{"alias_kind":"arxiv_version","alias_value":"2402.01033v1","created_at":"2026-07-05T07:40:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.01033","created_at":"2026-07-05T07:40:29Z"},{"alias_kind":"pith_short_12","alias_value":"UXX5FGWEUXVY","created_at":"2026-07-05T07:40:29Z"},{"alias_kind":"pith_short_16","alias_value":"UXX5FGWEUXVY6MRH","created_at":"2026-07-05T07:40:29Z"},{"alias_kind":"pith_short_8","alias_value":"UXX5FGWE","created_at":"2026-07-05T07:40:29Z"}],"graph_snapshots":[{"event_id":"sha256:1f8cfbf99af77edda1c168dcc116ae186f04f8eda04fbbbbc5d3367486131e24","target":"graph","created_at":"2026-07-05T07:40:29Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2402.01033/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This paper proposes and analyzes novel deep learning methods for downlink (DL) single-user multiple-input multiple-output (SU-MIMO) and multi-user MIMO (MU-MIMO) systems operating in time division duplex (TDD) mode. A motivating application is the 6G upper midbands (7-24 GHz), where the base station (BS) antenna arrays are large, user equipment (UE) array sizes are moderate, and theoretically optimal approaches are practically infeasible for several reasons. To deal with uplink (UL) pilot overhead and low signal power issues, we introduce the channel-adaptive pilot, as part of an analog channe","authors_text":"Amitava Ghosh, Foad Sohrabi, Jeffrey G. Andrews, Juseong Park","cross_cats":["eess.SP","math.IT"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2024-02-01T21:44:57Z","title":"End-to-End Deep Learning for TDD MIMO Systems in the 6G Upper Midbands"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.01033","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:6b2d85091055a185b7ad9a2502ef16b4c82b470a5ed04c35a959ecf0319d1f42","target":"record","created_at":"2026-07-05T07:40:29Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"c0d44eda245a80545552ba8e8d39a32a7f711e323cb160feadcf94ff27016625","cross_cats_sorted":["eess.SP","math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2024-02-01T21:44:57Z","title_canon_sha256":"8b3d7c2bb2551360f04e9dac84766cb0ef711fa9a490b78a7ec2a9f15bcc2df2"},"schema_version":"1.0","source":{"id":"2402.01033","kind":"arxiv","version":1}},"canonical_sha256":"a5efd29ac4a5eb8f32272c8259cbafdc31cf43a019b7d1f76822c265ed997877","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a5efd29ac4a5eb8f32272c8259cbafdc31cf43a019b7d1f76822c265ed997877","first_computed_at":"2026-07-05T07:40:29.645001Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:40:29.645001Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"4Ey3xNrxtqZQ4Gl81bwWQ1/DBSXleKH31wrVbqkAeJyLFhaIsl3+tefGwL6kJdacRLp1sd6gfo8PgarhW/l5Ag==","signature_status":"signed_v1","signed_at":"2026-07-05T07:40:29.645513Z","signed_message":"canonical_sha256_bytes"},"source_id":"2402.01033","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6b2d85091055a185b7ad9a2502ef16b4c82b470a5ed04c35a959ecf0319d1f42","sha256:1f8cfbf99af77edda1c168dcc116ae186f04f8eda04fbbbbc5d3367486131e24"],"state_sha256":"3aace426f8a2c2fb8364ad61a35cfd9c6b0913117cf61f6517f110fe087adebc"}