{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:X5652JX2PLO5HS67KM7OG2I22D","short_pith_number":"pith:X5652JX2","canonical_record":{"source":{"id":"1906.06021","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2019-06-14T04:50:13Z","cross_cats_sorted":["cs.IT","math.IT"],"title_canon_sha256":"719605a6537e04a947b7f05cb10a941b8b1ff1b1124717592c3c576a14d0a599","abstract_canon_sha256":"39fcf42919ee690bab9f6e003a0cd17e8db5d9f0294b88e7db17797ac484b379"},"schema_version":"1.0"},"canonical_sha256":"bf7ddd26fa7addd3cbdf533ee3691ad0f9c089b133026fa3db33bdcbc65a488a","source":{"kind":"arxiv","id":"1906.06021","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.06021","created_at":"2026-05-17T23:43:20Z"},{"alias_kind":"arxiv_version","alias_value":"1906.06021v1","created_at":"2026-05-17T23:43:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.06021","created_at":"2026-05-17T23:43:20Z"},{"alias_kind":"pith_short_12","alias_value":"X5652JX2PLO5","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_16","alias_value":"X5652JX2PLO5HS67","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_8","alias_value":"X5652JX2","created_at":"2026-05-18T12:33:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:X5652JX2PLO5HS67KM7OG2I22D","target":"record","payload":{"canonical_record":{"source":{"id":"1906.06021","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2019-06-14T04:50:13Z","cross_cats_sorted":["cs.IT","math.IT"],"title_canon_sha256":"719605a6537e04a947b7f05cb10a941b8b1ff1b1124717592c3c576a14d0a599","abstract_canon_sha256":"39fcf42919ee690bab9f6e003a0cd17e8db5d9f0294b88e7db17797ac484b379"},"schema_version":"1.0"},"canonical_sha256":"bf7ddd26fa7addd3cbdf533ee3691ad0f9c089b133026fa3db33bdcbc65a488a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:43:20.026378Z","signature_b64":"4Fe+Yn+acM391U6a5dh08jsa5u1sQwgq6SCm9UZnqBKn0Puh5Kj6NxiiuepFzSac5CKjRmdRgi8mOVWLVLtXAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bf7ddd26fa7addd3cbdf533ee3691ad0f9c089b133026fa3db33bdcbc65a488a","last_reissued_at":"2026-05-17T23:43:20.025741Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:43:20.025741Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1906.06021","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":"2026-05-17T23:43:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sTM1XHdmHhMYOc6AeQj/6dG9FoDj8jaLlu2assIWrE/tLl9MkmwpN+ODBcdsXBqNC8taqIktZvXCI1XlDYN+Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-24T14:07:01.524890Z"},"content_sha256":"a2f9efdc544b2a61f78e33045fb7a9a33afc2b6f23ffec2a7e79be62b669fd8e","schema_version":"1.0","event_id":"sha256:a2f9efdc544b2a61f78e33045fb7a9a33afc2b6f23ffec2a7e79be62b669fd8e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:X5652JX2PLO5HS67KM7OG2I22D","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Self-Tuning Sectorization: Deep Reinforcement Learning Meets Broadcast Beam Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","math.IT"],"primary_cat":"eess.SP","authors_text":"Hao Chen, Jeffrey Reed, Jeongho Park, Jianzhong (Charlie) Zhang, Lingjia Liu, Rubayet Shafin, Sooyoung Hur, Young Han Nam","submitted_at":"2019-06-14T04:50:13Z","abstract_excerpt":"Beamforming in multiple input multiple output (MIMO) systems is one of the key technologies for modern wireless communication. Creating appropriate sector-specific broadcast beams are essential for enhancing the coverage of cellular network and for improving the broadcast operation for control signals. However, in order to maximize the coverage, patterns for broadcast beams need to be adapted based on the users' distribution and movement over time. In this work, we present self-tuning sectorization: a deep reinforcement learning framework to optimize MIMO broadcast beams autonomously and dynam"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.06021","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":""},"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":"2026-05-17T23:43:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eyLeac5M1NGWbUpvEq/9F0bQTQnhHbhz0U/h4YJhga/uvwPJG13vY/jihMXUCpLZYlcYgJfwy9+lUaGauB/TBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-24T14:07:01.525332Z"},"content_sha256":"dd1c21ddc7196f26cb1e8a2b299b075b5e7a2145c73e31c8fb08066fdce6fcd9","schema_version":"1.0","event_id":"sha256:dd1c21ddc7196f26cb1e8a2b299b075b5e7a2145c73e31c8fb08066fdce6fcd9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/X5652JX2PLO5HS67KM7OG2I22D/bundle.json","state_url":"https://pith.science/pith/X5652JX2PLO5HS67KM7OG2I22D/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/X5652JX2PLO5HS67KM7OG2I22D/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":"2026-05-24T14:07:01Z","links":{"resolver":"https://pith.science/pith/X5652JX2PLO5HS67KM7OG2I22D","bundle":"https://pith.science/pith/X5652JX2PLO5HS67KM7OG2I22D/bundle.json","state":"https://pith.science/pith/X5652JX2PLO5HS67KM7OG2I22D/state.json","well_known_bundle":"https://pith.science/.well-known/pith/X5652JX2PLO5HS67KM7OG2I22D/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:X5652JX2PLO5HS67KM7OG2I22D","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":"39fcf42919ee690bab9f6e003a0cd17e8db5d9f0294b88e7db17797ac484b379","cross_cats_sorted":["cs.IT","math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2019-06-14T04:50:13Z","title_canon_sha256":"719605a6537e04a947b7f05cb10a941b8b1ff1b1124717592c3c576a14d0a599"},"schema_version":"1.0","source":{"id":"1906.06021","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.06021","created_at":"2026-05-17T23:43:20Z"},{"alias_kind":"arxiv_version","alias_value":"1906.06021v1","created_at":"2026-05-17T23:43:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.06021","created_at":"2026-05-17T23:43:20Z"},{"alias_kind":"pith_short_12","alias_value":"X5652JX2PLO5","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_16","alias_value":"X5652JX2PLO5HS67","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_8","alias_value":"X5652JX2","created_at":"2026-05-18T12:33:33Z"}],"graph_snapshots":[{"event_id":"sha256:dd1c21ddc7196f26cb1e8a2b299b075b5e7a2145c73e31c8fb08066fdce6fcd9","target":"graph","created_at":"2026-05-17T23:43:20Z","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"},"paper":{"abstract_excerpt":"Beamforming in multiple input multiple output (MIMO) systems is one of the key technologies for modern wireless communication. Creating appropriate sector-specific broadcast beams are essential for enhancing the coverage of cellular network and for improving the broadcast operation for control signals. However, in order to maximize the coverage, patterns for broadcast beams need to be adapted based on the users' distribution and movement over time. In this work, we present self-tuning sectorization: a deep reinforcement learning framework to optimize MIMO broadcast beams autonomously and dynam","authors_text":"Hao Chen, Jeffrey Reed, Jeongho Park, Jianzhong (Charlie) Zhang, Lingjia Liu, Rubayet Shafin, Sooyoung Hur, Young Han Nam","cross_cats":["cs.IT","math.IT"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2019-06-14T04:50:13Z","title":"Self-Tuning Sectorization: Deep Reinforcement Learning Meets Broadcast Beam Optimization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.06021","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:a2f9efdc544b2a61f78e33045fb7a9a33afc2b6f23ffec2a7e79be62b669fd8e","target":"record","created_at":"2026-05-17T23:43:20Z","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":"39fcf42919ee690bab9f6e003a0cd17e8db5d9f0294b88e7db17797ac484b379","cross_cats_sorted":["cs.IT","math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2019-06-14T04:50:13Z","title_canon_sha256":"719605a6537e04a947b7f05cb10a941b8b1ff1b1124717592c3c576a14d0a599"},"schema_version":"1.0","source":{"id":"1906.06021","kind":"arxiv","version":1}},"canonical_sha256":"bf7ddd26fa7addd3cbdf533ee3691ad0f9c089b133026fa3db33bdcbc65a488a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"bf7ddd26fa7addd3cbdf533ee3691ad0f9c089b133026fa3db33bdcbc65a488a","first_computed_at":"2026-05-17T23:43:20.025741Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:43:20.025741Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"4Fe+Yn+acM391U6a5dh08jsa5u1sQwgq6SCm9UZnqBKn0Puh5Kj6NxiiuepFzSac5CKjRmdRgi8mOVWLVLtXAA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:43:20.026378Z","signed_message":"canonical_sha256_bytes"},"source_id":"1906.06021","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a2f9efdc544b2a61f78e33045fb7a9a33afc2b6f23ffec2a7e79be62b669fd8e","sha256:dd1c21ddc7196f26cb1e8a2b299b075b5e7a2145c73e31c8fb08066fdce6fcd9"],"state_sha256":"efcbd49bc80105bd574e2b5e126b09daa096a7f3958e6a201bb7cf8da4288df6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iPzmZdDkTkCkKAUj6NBbWvPME9DZiF5gyJxPYWX+iIB9lXQswO2VGfw8A8uQJmcgONn9yAOfUR8b7iaeiKUWAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-24T14:07:01.528843Z","bundle_sha256":"d5c8740ce411b1d116cd7ee93b3e5d8adc34839f48c7a22271ca6c0c42959f7b"}}