{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:XEUKPN7S2DMG7B6N2YC7ETB22J","short_pith_number":"pith:XEUKPN7S","canonical_record":{"source":{"id":"2303.11096","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IT","submitted_at":"2023-03-20T13:32:37Z","cross_cats_sorted":["eess.SP","math.IT"],"title_canon_sha256":"d716837f86b7536f7d33da98bf19d219b74d96477e6d93363bed5990500fdb46","abstract_canon_sha256":"a7cb2fa94aba0275040b1db04ff6eda70672969b03d7de3cf21f3429dc6bfe79"},"schema_version":"1.0"},"canonical_sha256":"b928a7b7f2d0d86f87cdd605f24c3ad2575a589a5222e07e546af1301cef3c89","source":{"kind":"arxiv","id":"2303.11096","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2303.11096","created_at":"2026-07-05T05:52:40Z"},{"alias_kind":"arxiv_version","alias_value":"2303.11096v1","created_at":"2026-07-05T05:52:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.11096","created_at":"2026-07-05T05:52:40Z"},{"alias_kind":"pith_short_12","alias_value":"XEUKPN7S2DMG","created_at":"2026-07-05T05:52:40Z"},{"alias_kind":"pith_short_16","alias_value":"XEUKPN7S2DMG7B6N","created_at":"2026-07-05T05:52:40Z"},{"alias_kind":"pith_short_8","alias_value":"XEUKPN7S","created_at":"2026-07-05T05:52:40Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:XEUKPN7S2DMG7B6N2YC7ETB22J","target":"record","payload":{"canonical_record":{"source":{"id":"2303.11096","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IT","submitted_at":"2023-03-20T13:32:37Z","cross_cats_sorted":["eess.SP","math.IT"],"title_canon_sha256":"d716837f86b7536f7d33da98bf19d219b74d96477e6d93363bed5990500fdb46","abstract_canon_sha256":"a7cb2fa94aba0275040b1db04ff6eda70672969b03d7de3cf21f3429dc6bfe79"},"schema_version":"1.0"},"canonical_sha256":"b928a7b7f2d0d86f87cdd605f24c3ad2575a589a5222e07e546af1301cef3c89","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:52:40.238977Z","signature_b64":"JjQ/sl0w3RocdK5rp6u5b8/OFIN9cmBdMbxiXXEuzAvhaY3l6X4PGABzTrs+PwimFsy9hOuaFfRbtBKQ1xNmCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b928a7b7f2d0d86f87cdd605f24c3ad2575a589a5222e07e546af1301cef3c89","last_reissued_at":"2026-07-05T05:52:40.238562Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:52:40.238562Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2303.11096","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:52:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KbPZ3kBqprwzfV9pCCt3r24Ax0+cKGWHyvjg5vzv4hhoVe0bCiC6UFml1n9kU2UjOy9GphnXkrZW8p3BQHfNCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T16:33:01.040513Z"},"content_sha256":"c9697f05a6ed3cef9612f22a8fc95eb624fc316580ec55522109dbf1bc49ea51","schema_version":"1.0","event_id":"sha256:c9697f05a6ed3cef9612f22a8fc95eb624fc316580ec55522109dbf1bc49ea51"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:XEUKPN7S2DMG7B6N2YC7ETB22J","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Deep-Learning Aided Channel Training and Precoding in FDD Massive MIMO with Channel Statistics Knowledge","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["eess.SP","math.IT"],"primary_cat":"cs.IT","authors_text":"Giuseppe Caire, Mahdi Barzegar Khalilsarai, Tianyu Yang, Yi Song","submitted_at":"2023-03-20T13:32:37Z","abstract_excerpt":"We propose a method for channel training and precoding in FDD massive MIMO based on deep neural networks (DNNs), exploiting Downlink (DL) channel covariance knowledge. The DNN is optimized to maximize the DL multi-user sum-rate, by producing a pre-beamforming matrix based on user channel covariances that maps the original channel vectors to effective channels. Measurements of these effective channels are received at the users via common pilot transmission and sent back to the base station (BS) through analog feedback without further processing. The BS estimates the effective channels from rece"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.11096","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/2303.11096/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:52:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SWYHdeGBk0AKRbR/XotvO3Hkj8rKYaoS6e7OV7PY1/i0pGS/BAtM3dfYl3x0AORstX3P0FOYDWHL76purHT4DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T16:33:01.042867Z"},"content_sha256":"e5ce16856444d781ae3a828a887232badf7b30e52dee80a4feb2c6fb9171fde2","schema_version":"1.0","event_id":"sha256:e5ce16856444d781ae3a828a887232badf7b30e52dee80a4feb2c6fb9171fde2"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XEUKPN7S2DMG7B6N2YC7ETB22J/bundle.json","state_url":"https://pith.science/pith/XEUKPN7S2DMG7B6N2YC7ETB22J/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XEUKPN7S2DMG7B6N2YC7ETB22J/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-13T16:33:01Z","links":{"resolver":"https://pith.science/pith/XEUKPN7S2DMG7B6N2YC7ETB22J","bundle":"https://pith.science/pith/XEUKPN7S2DMG7B6N2YC7ETB22J/bundle.json","state":"https://pith.science/pith/XEUKPN7S2DMG7B6N2YC7ETB22J/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XEUKPN7S2DMG7B6N2YC7ETB22J/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:XEUKPN7S2DMG7B6N2YC7ETB22J","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":"a7cb2fa94aba0275040b1db04ff6eda70672969b03d7de3cf21f3429dc6bfe79","cross_cats_sorted":["eess.SP","math.IT"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IT","submitted_at":"2023-03-20T13:32:37Z","title_canon_sha256":"d716837f86b7536f7d33da98bf19d219b74d96477e6d93363bed5990500fdb46"},"schema_version":"1.0","source":{"id":"2303.11096","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2303.11096","created_at":"2026-07-05T05:52:40Z"},{"alias_kind":"arxiv_version","alias_value":"2303.11096v1","created_at":"2026-07-05T05:52:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.11096","created_at":"2026-07-05T05:52:40Z"},{"alias_kind":"pith_short_12","alias_value":"XEUKPN7S2DMG","created_at":"2026-07-05T05:52:40Z"},{"alias_kind":"pith_short_16","alias_value":"XEUKPN7S2DMG7B6N","created_at":"2026-07-05T05:52:40Z"},{"alias_kind":"pith_short_8","alias_value":"XEUKPN7S","created_at":"2026-07-05T05:52:40Z"}],"graph_snapshots":[{"event_id":"sha256:e5ce16856444d781ae3a828a887232badf7b30e52dee80a4feb2c6fb9171fde2","target":"graph","created_at":"2026-07-05T05:52:40Z","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/2303.11096/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We propose a method for channel training and precoding in FDD massive MIMO based on deep neural networks (DNNs), exploiting Downlink (DL) channel covariance knowledge. The DNN is optimized to maximize the DL multi-user sum-rate, by producing a pre-beamforming matrix based on user channel covariances that maps the original channel vectors to effective channels. Measurements of these effective channels are received at the users via common pilot transmission and sent back to the base station (BS) through analog feedback without further processing. The BS estimates the effective channels from rece","authors_text":"Giuseppe Caire, Mahdi Barzegar Khalilsarai, Tianyu Yang, Yi Song","cross_cats":["eess.SP","math.IT"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IT","submitted_at":"2023-03-20T13:32:37Z","title":"Deep-Learning Aided Channel Training and Precoding in FDD Massive MIMO with Channel Statistics Knowledge"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.11096","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:c9697f05a6ed3cef9612f22a8fc95eb624fc316580ec55522109dbf1bc49ea51","target":"record","created_at":"2026-07-05T05:52:40Z","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":"a7cb2fa94aba0275040b1db04ff6eda70672969b03d7de3cf21f3429dc6bfe79","cross_cats_sorted":["eess.SP","math.IT"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IT","submitted_at":"2023-03-20T13:32:37Z","title_canon_sha256":"d716837f86b7536f7d33da98bf19d219b74d96477e6d93363bed5990500fdb46"},"schema_version":"1.0","source":{"id":"2303.11096","kind":"arxiv","version":1}},"canonical_sha256":"b928a7b7f2d0d86f87cdd605f24c3ad2575a589a5222e07e546af1301cef3c89","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b928a7b7f2d0d86f87cdd605f24c3ad2575a589a5222e07e546af1301cef3c89","first_computed_at":"2026-07-05T05:52:40.238562Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:52:40.238562Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"JjQ/sl0w3RocdK5rp6u5b8/OFIN9cmBdMbxiXXEuzAvhaY3l6X4PGABzTrs+PwimFsy9hOuaFfRbtBKQ1xNmCw==","signature_status":"signed_v1","signed_at":"2026-07-05T05:52:40.238977Z","signed_message":"canonical_sha256_bytes"},"source_id":"2303.11096","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c9697f05a6ed3cef9612f22a8fc95eb624fc316580ec55522109dbf1bc49ea51","sha256:e5ce16856444d781ae3a828a887232badf7b30e52dee80a4feb2c6fb9171fde2"],"state_sha256":"1aec3668531524430e00e93bd5a43f16485516d13b2ca56df929db2cb2156f0f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qAtEbDaJBCMbwlRSFrJ9lPkx7334brdmN0Q2zSCPnoOWE5KT2GkLX3yDTPDY44pJY4NfQYDV9uKgVtv4DplABw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-13T16:33:01.058752Z","bundle_sha256":"f01b5f77de3b26721fa911e2d286eaf8f73dcdbe201ec40588597b358053e615"}}