{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:W64TYMPX67DMGDQGWNZ7AB2GIK","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":"e344c638407841287fb4e2481d49d4f1f51b10f438350236e9d1c68d664584bf","cross_cats_sorted":["cs.IT","math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2019-08-09T08:22:10Z","title_canon_sha256":"2f2f3a2bb431ce381400b0468ea0cbd55b74daae9d8b76ecc16f4787bcd8248f"},"schema_version":"1.0","source":{"id":"1908.03360","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1908.03360","created_at":"2026-07-04T23:59:34Z"},{"alias_kind":"arxiv_version","alias_value":"1908.03360v3","created_at":"2026-07-04T23:59:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.03360","created_at":"2026-07-04T23:59:34Z"},{"alias_kind":"pith_short_12","alias_value":"W64TYMPX67DM","created_at":"2026-07-04T23:59:34Z"},{"alias_kind":"pith_short_16","alias_value":"W64TYMPX67DMGDQG","created_at":"2026-07-04T23:59:34Z"},{"alias_kind":"pith_short_8","alias_value":"W64TYMPX","created_at":"2026-07-04T23:59:34Z"}],"graph_snapshots":[{"event_id":"sha256:f5b4659fdd7a74eace679825aabbdaf639f0de43c5b7351c8850829a56407410","target":"graph","created_at":"2026-07-04T23:59:34Z","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/1908.03360/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In a frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) system, the acquisition of downlink channel state information (CSI) at base station (BS) is a very challenging task due to the overwhelming overheads required for downlink training and uplink feedback. In this paper, we reveal a deterministic uplink-to-downlink mapping function when the position-to-channel mapping is bijective. Motivated by the universal approximation theorem, we then propose a sparse complex-valued neural network (SCNet) to approximate the uplink-to-downlink mapping function. Different from ","authors_text":"Feifei Gao, Geoffrey Ye Li, Mengnan Jian, Yuwen Yang","cross_cats":["cs.IT","math.IT"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2019-08-09T08:22:10Z","title":"Deep Learning based Downlink Channel Prediction for FDD Massive MIMO System"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.03360","kind":"arxiv","version":3},"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":"sha256:1fa3667b78929dc6d977f75b6d956e3443d92e5276291d06a0c225f2b2c8e2a5","target":"record","created_at":"2026-07-04T23:59:34Z","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":"e344c638407841287fb4e2481d49d4f1f51b10f438350236e9d1c68d664584bf","cross_cats_sorted":["cs.IT","math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2019-08-09T08:22:10Z","title_canon_sha256":"2f2f3a2bb431ce381400b0468ea0cbd55b74daae9d8b76ecc16f4787bcd8248f"},"schema_version":"1.0","source":{"id":"1908.03360","kind":"arxiv","version":3}},"canonical_sha256":"b7b93c31f7f7c6c30e06b373f007464287b6d17be12146ec411d0b71bd53de89","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b7b93c31f7f7c6c30e06b373f007464287b6d17be12146ec411d0b71bd53de89","first_computed_at":"2026-07-04T23:59:34.483650Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-04T23:59:34.483650Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"1/ODRHYFyGq30/tmR5ROtfIb1cOAZY76IFujEZMxf7Tgc2BM+ksTYU9wysATP7m3znlOTmeDGGOC8FLte/Q7Cw==","signature_status":"signed_v1","signed_at":"2026-07-04T23:59:34.484148Z","signed_message":"canonical_sha256_bytes"},"source_id":"1908.03360","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1fa3667b78929dc6d977f75b6d956e3443d92e5276291d06a0c225f2b2c8e2a5","sha256:f5b4659fdd7a74eace679825aabbdaf639f0de43c5b7351c8850829a56407410"],"state_sha256":"d6fd0b9b97aa6db8a4a7f2c615091a5ecdd5154e6d47a605271b6ff66782f299"}