{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:Y6W6NGV5TAA6JQ2DYID7Z54FBY","short_pith_number":"pith:Y6W6NGV5","canonical_record":{"source":{"id":"2104.01414","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IT","submitted_at":"2021-04-03T14:10:40Z","cross_cats_sorted":["cs.LG","math.IT"],"title_canon_sha256":"7e6dc6b2eabe52090b2b7575243e2dcfbc6b6817375a86b2e951546e6f2eddc8","abstract_canon_sha256":"c35ad87905a4a74d52cfaa8fc1fe615e48b81b0f37b9b4e274ced846c0851f85"},"schema_version":"1.0"},"canonical_sha256":"c7ade69abd9801e4c343c207fcf7850e1d7496936fad45f49ade5021cf50ff44","source":{"kind":"arxiv","id":"2104.01414","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2104.01414","created_at":"2026-07-05T02:28:59Z"},{"alias_kind":"arxiv_version","alias_value":"2104.01414v1","created_at":"2026-07-05T02:28:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2104.01414","created_at":"2026-07-05T02:28:59Z"},{"alias_kind":"pith_short_12","alias_value":"Y6W6NGV5TAA6","created_at":"2026-07-05T02:28:59Z"},{"alias_kind":"pith_short_16","alias_value":"Y6W6NGV5TAA6JQ2D","created_at":"2026-07-05T02:28:59Z"},{"alias_kind":"pith_short_8","alias_value":"Y6W6NGV5","created_at":"2026-07-05T02:28:59Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:Y6W6NGV5TAA6JQ2DYID7Z54FBY","target":"record","payload":{"canonical_record":{"source":{"id":"2104.01414","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IT","submitted_at":"2021-04-03T14:10:40Z","cross_cats_sorted":["cs.LG","math.IT"],"title_canon_sha256":"7e6dc6b2eabe52090b2b7575243e2dcfbc6b6817375a86b2e951546e6f2eddc8","abstract_canon_sha256":"c35ad87905a4a74d52cfaa8fc1fe615e48b81b0f37b9b4e274ced846c0851f85"},"schema_version":"1.0"},"canonical_sha256":"c7ade69abd9801e4c343c207fcf7850e1d7496936fad45f49ade5021cf50ff44","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:28:59.802611Z","signature_b64":"PcE+4DbLhLBVMIkjrPaWlVbKw3lmjdvUyJp3ZRaHBixqJLQYsplVUfO0zyHTZbQkVmj0wqAxu6x4x+oXSOiOCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c7ade69abd9801e4c343c207fcf7850e1d7496936fad45f49ade5021cf50ff44","last_reissued_at":"2026-07-05T02:28:59.802172Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:28:59.802172Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2104.01414","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":"2026-07-05T02:28:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RYBtu1dlor39fozmeid4gThon5taVTM74tPcRLPUcwyg5U4igE7HEsJUkJN2bMoU/99byxXapbsZq60olH3fCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T01:31:42.282837Z"},"content_sha256":"ae93fc24f79f065207ae1900c090e3ab1147f54e0d1f76b44c7209c523f38438","schema_version":"1.0","event_id":"sha256:ae93fc24f79f065207ae1900c090e3ab1147f54e0d1f76b44c7209c523f38438"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:Y6W6NGV5TAA6JQ2DYID7Z54FBY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Deep Reinforcement Learning Powered IRS-Assisted Downlink NOMA","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","math.IT"],"primary_cat":"cs.IT","authors_text":"Bekir S. Ciftler, Daniele Trinchero, Mohamed Abdallah, Muhammad Shehab, Tamer Khattab","submitted_at":"2021-04-03T14:10:40Z","abstract_excerpt":"In this work, we examine an intelligent reflecting surface (IRS) assisted downlink non-orthogonal multiple access (NOMA) scenario with the aim of maximizing the sum rate of users. The optimization problem at the IRS is quite complicated, and non-convex, since it requires the tuning of the phase shift reflection matrix. Driven by the rising deployment of deep reinforcement learning (DRL) techniques that are capable of coping with solving non-convex optimization problems, we employ DRL to predict and optimally tune the IRS phase shift matrices. Simulation results reveal that IRS assisted NOMA ba"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2104.01414","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/2104.01414/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":"2026-07-05T02:28:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KoJeOQS+wVRF/itFdaOZRjMwxqUlcSw36t/zzeEK9JpA/0yQ5q6m55PoZDQMRhn8QsaiKbBMJHizNyKTNYysCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T01:31:42.283461Z"},"content_sha256":"d28fc89ea4d5859252a51c4a3434066835d24bf5f16d121c0d8502fc66bf2368","schema_version":"1.0","event_id":"sha256:d28fc89ea4d5859252a51c4a3434066835d24bf5f16d121c0d8502fc66bf2368"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/Y6W6NGV5TAA6JQ2DYID7Z54FBY/bundle.json","state_url":"https://pith.science/pith/Y6W6NGV5TAA6JQ2DYID7Z54FBY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/Y6W6NGV5TAA6JQ2DYID7Z54FBY/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":"2026-08-17T01:31:42Z","links":{"resolver":"https://pith.science/pith/Y6W6NGV5TAA6JQ2DYID7Z54FBY","bundle":"https://pith.science/pith/Y6W6NGV5TAA6JQ2DYID7Z54FBY/bundle.json","state":"https://pith.science/pith/Y6W6NGV5TAA6JQ2DYID7Z54FBY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/Y6W6NGV5TAA6JQ2DYID7Z54FBY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:Y6W6NGV5TAA6JQ2DYID7Z54FBY","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":"c35ad87905a4a74d52cfaa8fc1fe615e48b81b0f37b9b4e274ced846c0851f85","cross_cats_sorted":["cs.LG","math.IT"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IT","submitted_at":"2021-04-03T14:10:40Z","title_canon_sha256":"7e6dc6b2eabe52090b2b7575243e2dcfbc6b6817375a86b2e951546e6f2eddc8"},"schema_version":"1.0","source":{"id":"2104.01414","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2104.01414","created_at":"2026-07-05T02:28:59Z"},{"alias_kind":"arxiv_version","alias_value":"2104.01414v1","created_at":"2026-07-05T02:28:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2104.01414","created_at":"2026-07-05T02:28:59Z"},{"alias_kind":"pith_short_12","alias_value":"Y6W6NGV5TAA6","created_at":"2026-07-05T02:28:59Z"},{"alias_kind":"pith_short_16","alias_value":"Y6W6NGV5TAA6JQ2D","created_at":"2026-07-05T02:28:59Z"},{"alias_kind":"pith_short_8","alias_value":"Y6W6NGV5","created_at":"2026-07-05T02:28:59Z"}],"graph_snapshots":[{"event_id":"sha256:d28fc89ea4d5859252a51c4a3434066835d24bf5f16d121c0d8502fc66bf2368","target":"graph","created_at":"2026-07-05T02:28:59Z","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/2104.01414/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this work, we examine an intelligent reflecting surface (IRS) assisted downlink non-orthogonal multiple access (NOMA) scenario with the aim of maximizing the sum rate of users. The optimization problem at the IRS is quite complicated, and non-convex, since it requires the tuning of the phase shift reflection matrix. Driven by the rising deployment of deep reinforcement learning (DRL) techniques that are capable of coping with solving non-convex optimization problems, we employ DRL to predict and optimally tune the IRS phase shift matrices. Simulation results reveal that IRS assisted NOMA ba","authors_text":"Bekir S. Ciftler, Daniele Trinchero, Mohamed Abdallah, Muhammad Shehab, Tamer Khattab","cross_cats":["cs.LG","math.IT"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IT","submitted_at":"2021-04-03T14:10:40Z","title":"Deep Reinforcement Learning Powered IRS-Assisted Downlink NOMA"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2104.01414","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:ae93fc24f79f065207ae1900c090e3ab1147f54e0d1f76b44c7209c523f38438","target":"record","created_at":"2026-07-05T02:28:59Z","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":"c35ad87905a4a74d52cfaa8fc1fe615e48b81b0f37b9b4e274ced846c0851f85","cross_cats_sorted":["cs.LG","math.IT"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IT","submitted_at":"2021-04-03T14:10:40Z","title_canon_sha256":"7e6dc6b2eabe52090b2b7575243e2dcfbc6b6817375a86b2e951546e6f2eddc8"},"schema_version":"1.0","source":{"id":"2104.01414","kind":"arxiv","version":1}},"canonical_sha256":"c7ade69abd9801e4c343c207fcf7850e1d7496936fad45f49ade5021cf50ff44","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c7ade69abd9801e4c343c207fcf7850e1d7496936fad45f49ade5021cf50ff44","first_computed_at":"2026-07-05T02:28:59.802172Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:28:59.802172Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"PcE+4DbLhLBVMIkjrPaWlVbKw3lmjdvUyJp3ZRaHBixqJLQYsplVUfO0zyHTZbQkVmj0wqAxu6x4x+oXSOiOCg==","signature_status":"signed_v1","signed_at":"2026-07-05T02:28:59.802611Z","signed_message":"canonical_sha256_bytes"},"source_id":"2104.01414","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ae93fc24f79f065207ae1900c090e3ab1147f54e0d1f76b44c7209c523f38438","sha256:d28fc89ea4d5859252a51c4a3434066835d24bf5f16d121c0d8502fc66bf2368"],"state_sha256":"1ec6c4413a5bbe451cdb71f2d4b80ece7fc665250b26a83b40c4759e9b30ecad"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iaWv4RZTruiKxGyvOKTxpxdaRuHfloZELGQ6hsMpqHVgNsfEKUQJ/Z5hBLUNFezFnbbPKO4V0+06zYM372CcCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-17T01:31:42.289357Z","bundle_sha256":"e0cebe48cd72a8e47e1e344a85b9536ef4f40a03f3bf746b3a943dec4325dbb2"}}