{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:JQPOYXZZZQHRXSBVD6GAWHOTN3","short_pith_number":"pith:JQPOYXZZ","schema_version":"1.0","canonical_sha256":"4c1eec5f39cc0f1bc8351f8c0b1dd36ef9b13b4fe13c27306768aebecca0c60d","source":{"kind":"arxiv","id":"1902.06457","version":1},"attestation_state":"computed","paper":{"title":"Simple Approximations of the SIR Meta Distribution in General Cellular Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NI","math.IT"],"primary_cat":"cs.IT","authors_text":"Martin Haenggi, Sanket S. Kalamkar","submitted_at":"2019-02-18T08:31:53Z","abstract_excerpt":"Compared to the standard success (coverage) probability, the meta distribution of the signal-to-interference ratio (SIR) provides much more fine-grained information about the network performance. We consider general heterogeneous cellular networks (HCNs) with base station tiers modeled by arbitrary stationary and ergodic non-Poisson point processes. The exact analysis of non-Poisson network models is notoriously difficult, even in terms of the standard success probability, let alone the meta distribution. Hence we propose a simple approach to approximate the SIR meta distribution for non-Poiss"},"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":"1902.06457","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2019-02-18T08:31:53Z","cross_cats_sorted":["cs.NI","math.IT"],"title_canon_sha256":"c1f03bd6e7d1fe546af7e94df889866bcb49c3a397142e5eba71b7fbcbf30565","abstract_canon_sha256":"b068c61027fb13c168a24640e0434961e4e052fdfc342e3dee4a66563510d3df"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:53:45.414465Z","signature_b64":"dNLuUacvkiz8qYIlwl3d1vRcDAHJonCaEjeahSihyy3D7XKGQXYUGcb3y7Q4tozklrnR3X8qVgWjHR32hCYODQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4c1eec5f39cc0f1bc8351f8c0b1dd36ef9b13b4fe13c27306768aebecca0c60d","last_reissued_at":"2026-05-17T23:53:45.413734Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:53:45.413734Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Simple Approximations of the SIR Meta Distribution in General Cellular Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NI","math.IT"],"primary_cat":"cs.IT","authors_text":"Martin Haenggi, Sanket S. Kalamkar","submitted_at":"2019-02-18T08:31:53Z","abstract_excerpt":"Compared to the standard success (coverage) probability, the meta distribution of the signal-to-interference ratio (SIR) provides much more fine-grained information about the network performance. We consider general heterogeneous cellular networks (HCNs) with base station tiers modeled by arbitrary stationary and ergodic non-Poisson point processes. The exact analysis of non-Poisson network models is notoriously difficult, even in terms of the standard success probability, let alone the meta distribution. Hence we propose a simple approach to approximate the SIR meta distribution for non-Poiss"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.06457","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1902.06457","created_at":"2026-05-17T23:53:45.413846+00:00"},{"alias_kind":"arxiv_version","alias_value":"1902.06457v1","created_at":"2026-05-17T23:53:45.413846+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.06457","created_at":"2026-05-17T23:53:45.413846+00:00"},{"alias_kind":"pith_short_12","alias_value":"JQPOYXZZZQHR","created_at":"2026-05-18T12:33:21.387695+00:00"},{"alias_kind":"pith_short_16","alias_value":"JQPOYXZZZQHRXSBV","created_at":"2026-05-18T12:33:21.387695+00:00"},{"alias_kind":"pith_short_8","alias_value":"JQPOYXZZ","created_at":"2026-05-18T12:33:21.387695+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/JQPOYXZZZQHRXSBVD6GAWHOTN3","json":"https://pith.science/pith/JQPOYXZZZQHRXSBVD6GAWHOTN3.json","graph_json":"https://pith.science/api/pith-number/JQPOYXZZZQHRXSBVD6GAWHOTN3/graph.json","events_json":"https://pith.science/api/pith-number/JQPOYXZZZQHRXSBVD6GAWHOTN3/events.json","paper":"https://pith.science/paper/JQPOYXZZ"},"agent_actions":{"view_html":"https://pith.science/pith/JQPOYXZZZQHRXSBVD6GAWHOTN3","download_json":"https://pith.science/pith/JQPOYXZZZQHRXSBVD6GAWHOTN3.json","view_paper":"https://pith.science/paper/JQPOYXZZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1902.06457&json=true","fetch_graph":"https://pith.science/api/pith-number/JQPOYXZZZQHRXSBVD6GAWHOTN3/graph.json","fetch_events":"https://pith.science/api/pith-number/JQPOYXZZZQHRXSBVD6GAWHOTN3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JQPOYXZZZQHRXSBVD6GAWHOTN3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JQPOYXZZZQHRXSBVD6GAWHOTN3/action/storage_attestation","attest_author":"https://pith.science/pith/JQPOYXZZZQHRXSBVD6GAWHOTN3/action/author_attestation","sign_citation":"https://pith.science/pith/JQPOYXZZZQHRXSBVD6GAWHOTN3/action/citation_signature","submit_replication":"https://pith.science/pith/JQPOYXZZZQHRXSBVD6GAWHOTN3/action/replication_record"}},"created_at":"2026-05-17T23:53:45.413846+00:00","updated_at":"2026-05-17T23:53:45.413846+00:00"}