{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:XH7HDXWKBV7DFZIE7SHVYNLVBM","short_pith_number":"pith:XH7HDXWK","schema_version":"1.0","canonical_sha256":"b9fe71deca0d7e32e504fc8f5c35750b23db1498f6639cf12fb370c5ea9eecf4","source":{"kind":"arxiv","id":"2106.00574","version":1},"attestation_state":"computed","paper":{"title":"Deep Reinforcement Learning for Radio Resource Allocation and Management in Next Generation Heterogeneous Wireless Networks: A Survey","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SY","eess.SY"],"primary_cat":"eess.SP","authors_text":"Abdulmalik Alwarafy, Ala Al-Fuqaha, Bekir Sait Ciftler, Mohamed Abdallah, Mounir Hamdi","submitted_at":"2021-05-25T19:41:40Z","abstract_excerpt":"Next generation wireless networks are expected to be extremely complex due to their massive heterogeneity in terms of the types of network architectures they incorporate, the types and numbers of smart IoT devices they serve, and the types of emerging applications they support. In such large-scale and heterogeneous networks (HetNets), radio resource allocation and management (RRAM) becomes one of the major challenges encountered during system design and deployment. In this context, emerging Deep Reinforcement Learning (DRL) techniques are expected to be one of the main enabling technologies to"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2106.00574","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SP","submitted_at":"2021-05-25T19:41:40Z","cross_cats_sorted":["cs.SY","eess.SY"],"title_canon_sha256":"c5952a77d2413c9b3b48f71869c268b3da090c9a6d47e253327ece15616978c3","abstract_canon_sha256":"4774d7e8176a057c4cbd8f5a2776fea57ef6750365b2b965a4c4f586c8b0c778"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:03:46.987691Z","signature_b64":"Q75TJREut8Lng6GnsJK3JY6Vzieu0ok9wYeogP7VVWFaEr/KvxWmfQrFEO3whAeCMW7xL54zHtgI1QtDGpMUCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b9fe71deca0d7e32e504fc8f5c35750b23db1498f6639cf12fb370c5ea9eecf4","last_reissued_at":"2026-07-05T04:03:46.987204Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:03:46.987204Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Reinforcement Learning for Radio Resource Allocation and Management in Next Generation Heterogeneous Wireless Networks: A Survey","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SY","eess.SY"],"primary_cat":"eess.SP","authors_text":"Abdulmalik Alwarafy, Ala Al-Fuqaha, Bekir Sait Ciftler, Mohamed Abdallah, Mounir Hamdi","submitted_at":"2021-05-25T19:41:40Z","abstract_excerpt":"Next generation wireless networks are expected to be extremely complex due to their massive heterogeneity in terms of the types of network architectures they incorporate, the types and numbers of smart IoT devices they serve, and the types of emerging applications they support. In such large-scale and heterogeneous networks (HetNets), radio resource allocation and management (RRAM) becomes one of the major challenges encountered during system design and deployment. In this context, emerging Deep Reinforcement Learning (DRL) techniques are expected to be one of the main enabling technologies to"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2106.00574","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/2106.00574/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2106.00574","created_at":"2026-07-05T04:03:46.987259+00:00"},{"alias_kind":"arxiv_version","alias_value":"2106.00574v1","created_at":"2026-07-05T04:03:46.987259+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.00574","created_at":"2026-07-05T04:03:46.987259+00:00"},{"alias_kind":"pith_short_12","alias_value":"XH7HDXWKBV7D","created_at":"2026-07-05T04:03:46.987259+00:00"},{"alias_kind":"pith_short_16","alias_value":"XH7HDXWKBV7DFZIE","created_at":"2026-07-05T04:03:46.987259+00:00"},{"alias_kind":"pith_short_8","alias_value":"XH7HDXWK","created_at":"2026-07-05T04:03:46.987259+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.05246","citing_title":"Bounded Deep Unfolding for Joint Beamforming and Scheduling in Multi-Cell MIMO Networks","ref_index":21,"is_internal_anchor":false},{"citing_arxiv_id":"2605.25531","citing_title":"From Denoising to Decision Making: A Survey on Diffusion Model-Enabled Deep Reinforcement Learning for Wireless Networks","ref_index":37,"is_internal_anchor":false},{"citing_arxiv_id":"2606.05208","citing_title":"Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks","ref_index":4,"is_internal_anchor":false},{"citing_arxiv_id":"2604.22706","citing_title":"When AI Meets Terahertz: A Survey on the Symbiosis of Artificial Intelligence and Terahertz Networks","ref_index":60,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/XH7HDXWKBV7DFZIE7SHVYNLVBM","json":"https://pith.science/pith/XH7HDXWKBV7DFZIE7SHVYNLVBM.json","graph_json":"https://pith.science/api/pith-number/XH7HDXWKBV7DFZIE7SHVYNLVBM/graph.json","events_json":"https://pith.science/api/pith-number/XH7HDXWKBV7DFZIE7SHVYNLVBM/events.json","paper":"https://pith.science/paper/XH7HDXWK"},"agent_actions":{"view_html":"https://pith.science/pith/XH7HDXWKBV7DFZIE7SHVYNLVBM","download_json":"https://pith.science/pith/XH7HDXWKBV7DFZIE7SHVYNLVBM.json","view_paper":"https://pith.science/paper/XH7HDXWK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2106.00574&json=true","fetch_graph":"https://pith.science/api/pith-number/XH7HDXWKBV7DFZIE7SHVYNLVBM/graph.json","fetch_events":"https://pith.science/api/pith-number/XH7HDXWKBV7DFZIE7SHVYNLVBM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XH7HDXWKBV7DFZIE7SHVYNLVBM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XH7HDXWKBV7DFZIE7SHVYNLVBM/action/storage_attestation","attest_author":"https://pith.science/pith/XH7HDXWKBV7DFZIE7SHVYNLVBM/action/author_attestation","sign_citation":"https://pith.science/pith/XH7HDXWKBV7DFZIE7SHVYNLVBM/action/citation_signature","submit_replication":"https://pith.science/pith/XH7HDXWKBV7DFZIE7SHVYNLVBM/action/replication_record"}},"created_at":"2026-07-05T04:03:46.987259+00:00","updated_at":"2026-07-05T04:03:46.987259+00:00"}