{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:EPVJJ3OG3WL4U24COAVX7XPEB2","short_pith_number":"pith:EPVJJ3OG","schema_version":"1.0","canonical_sha256":"23ea94edc6dd97ca6b82702b7fdde40e92a84f93e87ee0920a40277ee450202e","source":{"kind":"arxiv","id":"2202.13187","version":2},"attestation_state":"computed","paper":{"title":"Whittle Index based Q-Learning for Wireless Edge Caching with Linear Function Approximation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.NI","authors_text":"Guojun Xiong, Jian Li, Rahul Singh, Shufan Wang","submitted_at":"2022-02-26T17:01:24Z","abstract_excerpt":"We consider the problem of content caching at the wireless edge to serve a set of end users via unreliable wireless channels so as to minimize the average latency experienced by end users due to the constrained wireless edge cache capacity. We formulate this problem as a Markov decision process, or more specifically a restless multi-armed bandit problem, which is provably hard to solve. We begin by investigating a discounted counterpart, and prove that it admits an optimal policy of the threshold-type. We then show that this result also holds for average latency problem. Using this structural "},"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":"2202.13187","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NI","submitted_at":"2022-02-26T17:01:24Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"2d1b60e5cebe2c04a34a5214061657f4c161f4b95b9c0e10a591f784835f1470","abstract_canon_sha256":"8bd71ed38ccb07599590f6ff9bd4d739a937bbd33802c03562a68217b82c2dea"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:44:47.983313Z","signature_b64":"6VwBo5ReHkWqEgHfC87JPhkx1Bdn9QVpubvB84AIBqZAZwyMxIxGodYaWJMDR0p+XeDZ7jtvYxbbW0FRPVbzBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"23ea94edc6dd97ca6b82702b7fdde40e92a84f93e87ee0920a40277ee450202e","last_reissued_at":"2026-07-05T05:44:47.982887Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:44:47.982887Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Whittle Index based Q-Learning for Wireless Edge Caching with Linear Function Approximation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.NI","authors_text":"Guojun Xiong, Jian Li, Rahul Singh, Shufan Wang","submitted_at":"2022-02-26T17:01:24Z","abstract_excerpt":"We consider the problem of content caching at the wireless edge to serve a set of end users via unreliable wireless channels so as to minimize the average latency experienced by end users due to the constrained wireless edge cache capacity. We formulate this problem as a Markov decision process, or more specifically a restless multi-armed bandit problem, which is provably hard to solve. We begin by investigating a discounted counterpart, and prove that it admits an optimal policy of the threshold-type. We then show that this result also holds for average latency problem. Using this structural "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.13187","kind":"arxiv","version":2},"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/2202.13187/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":"2202.13187","created_at":"2026-07-05T05:44:47.982943+00:00"},{"alias_kind":"arxiv_version","alias_value":"2202.13187v2","created_at":"2026-07-05T05:44:47.982943+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.13187","created_at":"2026-07-05T05:44:47.982943+00:00"},{"alias_kind":"pith_short_12","alias_value":"EPVJJ3OG3WL4","created_at":"2026-07-05T05:44:47.982943+00:00"},{"alias_kind":"pith_short_16","alias_value":"EPVJJ3OG3WL4U24C","created_at":"2026-07-05T05:44:47.982943+00:00"},{"alias_kind":"pith_short_8","alias_value":"EPVJJ3OG","created_at":"2026-07-05T05:44:47.982943+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EPVJJ3OG3WL4U24COAVX7XPEB2","json":"https://pith.science/pith/EPVJJ3OG3WL4U24COAVX7XPEB2.json","graph_json":"https://pith.science/api/pith-number/EPVJJ3OG3WL4U24COAVX7XPEB2/graph.json","events_json":"https://pith.science/api/pith-number/EPVJJ3OG3WL4U24COAVX7XPEB2/events.json","paper":"https://pith.science/paper/EPVJJ3OG"},"agent_actions":{"view_html":"https://pith.science/pith/EPVJJ3OG3WL4U24COAVX7XPEB2","download_json":"https://pith.science/pith/EPVJJ3OG3WL4U24COAVX7XPEB2.json","view_paper":"https://pith.science/paper/EPVJJ3OG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2202.13187&json=true","fetch_graph":"https://pith.science/api/pith-number/EPVJJ3OG3WL4U24COAVX7XPEB2/graph.json","fetch_events":"https://pith.science/api/pith-number/EPVJJ3OG3WL4U24COAVX7XPEB2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EPVJJ3OG3WL4U24COAVX7XPEB2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EPVJJ3OG3WL4U24COAVX7XPEB2/action/storage_attestation","attest_author":"https://pith.science/pith/EPVJJ3OG3WL4U24COAVX7XPEB2/action/author_attestation","sign_citation":"https://pith.science/pith/EPVJJ3OG3WL4U24COAVX7XPEB2/action/citation_signature","submit_replication":"https://pith.science/pith/EPVJJ3OG3WL4U24COAVX7XPEB2/action/replication_record"}},"created_at":"2026-07-05T05:44:47.982943+00:00","updated_at":"2026-07-05T05:44:47.982943+00:00"}