{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:27FOVVAXEZE2NFORTFMGJP4ZZQ","short_pith_number":"pith:27FOVVAX","canonical_record":{"source":{"id":"2008.11006","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2020-08-25T13:45:13Z","cross_cats_sorted":["cs.IT","cs.NI","math.IT"],"title_canon_sha256":"45b33cf96bedacf598a36f164340a954025258944057d3c43e6486b7841e3350","abstract_canon_sha256":"d039646c5e4c988316d194a71169395893ec17401965b439f88750b29823bc83"},"schema_version":"1.0"},"canonical_sha256":"d7caead4172649a695d1995864bf99cc0c4fb9cb9d2aef9130a31bf46e2e5cf1","source":{"kind":"arxiv","id":"2008.11006","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2008.11006","created_at":"2026-07-05T02:00:12Z"},{"alias_kind":"arxiv_version","alias_value":"2008.11006v1","created_at":"2026-07-05T02:00:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2008.11006","created_at":"2026-07-05T02:00:12Z"},{"alias_kind":"pith_short_12","alias_value":"27FOVVAXEZE2","created_at":"2026-07-05T02:00:12Z"},{"alias_kind":"pith_short_16","alias_value":"27FOVVAXEZE2NFOR","created_at":"2026-07-05T02:00:12Z"},{"alias_kind":"pith_short_8","alias_value":"27FOVVAX","created_at":"2026-07-05T02:00:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:27FOVVAXEZE2NFORTFMGJP4ZZQ","target":"record","payload":{"canonical_record":{"source":{"id":"2008.11006","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2020-08-25T13:45:13Z","cross_cats_sorted":["cs.IT","cs.NI","math.IT"],"title_canon_sha256":"45b33cf96bedacf598a36f164340a954025258944057d3c43e6486b7841e3350","abstract_canon_sha256":"d039646c5e4c988316d194a71169395893ec17401965b439f88750b29823bc83"},"schema_version":"1.0"},"canonical_sha256":"d7caead4172649a695d1995864bf99cc0c4fb9cb9d2aef9130a31bf46e2e5cf1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:00:12.654197Z","signature_b64":"dl+i3y3I+WgOFRt86j4md87y6f3yCl1Mx84Wj3jrse+ltp74Q4ckFzbz9UKpkNTfvuc1gQxTdEzKuKVL1b4XAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d7caead4172649a695d1995864bf99cc0c4fb9cb9d2aef9130a31bf46e2e5cf1","last_reissued_at":"2026-07-05T02:00:12.653627Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:00:12.653627Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2008.11006","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":"2026-07-05T02:00:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"30q2pX8Cib2V+flXsP2DCO/28jqIKJOI3r9H1FK6PHexxdbVlBDYWkSw+0sOnwVxXRbyyTSzAJmdt6I4PhkcAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T17:41:38.782477Z"},"content_sha256":"bea39b80a318731d15a33dcc5d515647cd7dcf934460bd61fcc46f6b7f48982d","schema_version":"1.0","event_id":"sha256:bea39b80a318731d15a33dcc5d515647cd7dcf934460bd61fcc46f6b7f48982d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:27FOVVAXEZE2NFORTFMGJP4ZZQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Millimeter Wave Channel Modeling via Generative Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","cs.NI","math.IT"],"primary_cat":"eess.SP","authors_text":"Angel Lozano, Giovanni Geraci, Giuseppe Loianno, Marco Mezzavilla, Sundeep Rangan, Vasilii Semkin, William Xia","submitted_at":"2020-08-25T13:45:13Z","abstract_excerpt":"Statistical channel models are instrumental to design and evaluate wireless communication systems. In the millimeter wave bands, such models become acutely challenging; they must capture the delay, directions, and path gains, for each link and with high resolution. This paper presents a general modeling methodology based on training generative neural networks from data. The proposed generative model consists of a two-stage structure that first predicts the state of each link (line-of-sight, non-line-of-sight, or outage), and subsequently feeds this state into a conditional variational autoenco"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2008.11006","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/2008.11006/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":"2026-07-05T02:00:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"l6T33zioJoHAGk19i1Uh3TlsxvW715fKaCPPtOIIbuO4te3Lzd6V/9Psbj95eKPKF9dvCbEbXOkOzawIZVlDCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T17:41:38.782990Z"},"content_sha256":"563e2dee59551b495e4b53b285d4f809f9d99c48150255283b3dc155b7197c0c","schema_version":"1.0","event_id":"sha256:563e2dee59551b495e4b53b285d4f809f9d99c48150255283b3dc155b7197c0c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/27FOVVAXEZE2NFORTFMGJP4ZZQ/bundle.json","state_url":"https://pith.science/pith/27FOVVAXEZE2NFORTFMGJP4ZZQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/27FOVVAXEZE2NFORTFMGJP4ZZQ/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":"2026-08-14T17:41:38Z","links":{"resolver":"https://pith.science/pith/27FOVVAXEZE2NFORTFMGJP4ZZQ","bundle":"https://pith.science/pith/27FOVVAXEZE2NFORTFMGJP4ZZQ/bundle.json","state":"https://pith.science/pith/27FOVVAXEZE2NFORTFMGJP4ZZQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/27FOVVAXEZE2NFORTFMGJP4ZZQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:27FOVVAXEZE2NFORTFMGJP4ZZQ","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":"d039646c5e4c988316d194a71169395893ec17401965b439f88750b29823bc83","cross_cats_sorted":["cs.IT","cs.NI","math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2020-08-25T13:45:13Z","title_canon_sha256":"45b33cf96bedacf598a36f164340a954025258944057d3c43e6486b7841e3350"},"schema_version":"1.0","source":{"id":"2008.11006","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2008.11006","created_at":"2026-07-05T02:00:12Z"},{"alias_kind":"arxiv_version","alias_value":"2008.11006v1","created_at":"2026-07-05T02:00:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2008.11006","created_at":"2026-07-05T02:00:12Z"},{"alias_kind":"pith_short_12","alias_value":"27FOVVAXEZE2","created_at":"2026-07-05T02:00:12Z"},{"alias_kind":"pith_short_16","alias_value":"27FOVVAXEZE2NFOR","created_at":"2026-07-05T02:00:12Z"},{"alias_kind":"pith_short_8","alias_value":"27FOVVAX","created_at":"2026-07-05T02:00:12Z"}],"graph_snapshots":[{"event_id":"sha256:563e2dee59551b495e4b53b285d4f809f9d99c48150255283b3dc155b7197c0c","target":"graph","created_at":"2026-07-05T02:00:12Z","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/2008.11006/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Statistical channel models are instrumental to design and evaluate wireless communication systems. In the millimeter wave bands, such models become acutely challenging; they must capture the delay, directions, and path gains, for each link and with high resolution. This paper presents a general modeling methodology based on training generative neural networks from data. The proposed generative model consists of a two-stage structure that first predicts the state of each link (line-of-sight, non-line-of-sight, or outage), and subsequently feeds this state into a conditional variational autoenco","authors_text":"Angel Lozano, Giovanni Geraci, Giuseppe Loianno, Marco Mezzavilla, Sundeep Rangan, Vasilii Semkin, William Xia","cross_cats":["cs.IT","cs.NI","math.IT"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2020-08-25T13:45:13Z","title":"Millimeter Wave Channel Modeling via Generative Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2008.11006","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:bea39b80a318731d15a33dcc5d515647cd7dcf934460bd61fcc46f6b7f48982d","target":"record","created_at":"2026-07-05T02:00:12Z","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":"d039646c5e4c988316d194a71169395893ec17401965b439f88750b29823bc83","cross_cats_sorted":["cs.IT","cs.NI","math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2020-08-25T13:45:13Z","title_canon_sha256":"45b33cf96bedacf598a36f164340a954025258944057d3c43e6486b7841e3350"},"schema_version":"1.0","source":{"id":"2008.11006","kind":"arxiv","version":1}},"canonical_sha256":"d7caead4172649a695d1995864bf99cc0c4fb9cb9d2aef9130a31bf46e2e5cf1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d7caead4172649a695d1995864bf99cc0c4fb9cb9d2aef9130a31bf46e2e5cf1","first_computed_at":"2026-07-05T02:00:12.653627Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:00:12.653627Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"dl+i3y3I+WgOFRt86j4md87y6f3yCl1Mx84Wj3jrse+ltp74Q4ckFzbz9UKpkNTfvuc1gQxTdEzKuKVL1b4XAg==","signature_status":"signed_v1","signed_at":"2026-07-05T02:00:12.654197Z","signed_message":"canonical_sha256_bytes"},"source_id":"2008.11006","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bea39b80a318731d15a33dcc5d515647cd7dcf934460bd61fcc46f6b7f48982d","sha256:563e2dee59551b495e4b53b285d4f809f9d99c48150255283b3dc155b7197c0c"],"state_sha256":"61d4acc4e3b57a8b71eeb122c1cbdb2543ce6725ed7e8f8e66d459170d799ce0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PtgZyt5YY+h8nMoTb57CcjSXgB68WpBLo1Db8FLRep6oHmrl0WZa1tP/l0h6TdDERyNWcZjmK4IpQBOCoH8QBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-14T17:41:38.788425Z","bundle_sha256":"97491886fee99b95a623471fc73948e4de4e47c613fd24075bd384e25de8679c"}}