{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:UC2ZQLLZXR4OKMYCZPNPQ4GARO","short_pith_number":"pith:UC2ZQLLZ","schema_version":"1.0","canonical_sha256":"a0b5982d79bc78e53302cbdaf870c08bb18a09d91359f44e0940c4e4b750e573","source":{"kind":"arxiv","id":"1612.06809","version":1},"attestation_state":"computed","paper":{"title":"The Matrix Exponential Distribution - A Tool for Wireless System Performance Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Lars K. Rasmussen, Mikael Skoglund, Peter Larsson","submitted_at":"2016-12-20T18:56:30Z","abstract_excerpt":"In [1], we introduced a new, matrix algebraic, performance analysis framework for wireless systems with fading channels based on the matrix exponential distribution. The main idea was to use the compact, powerful, and easy-to-use, matrix exponential (ME)-distribution for i) modeling the unprocessed channel signal to noise ratio (SNR), ii) exploiting the closure property of the ME-distribution for SNR processing operations to give the effective channel random variable (r.v.) on ME-distribution form, and then to iii) express the performance measure in a closed-form based on ME-distribution matri"},"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":"1612.06809","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2016-12-20T18:56:30Z","cross_cats_sorted":["math.IT"],"title_canon_sha256":"cdaa08fb611d0efddc732681a2d192e9b7c2a4cce27bdc121e8eebe1fb5f7b34","abstract_canon_sha256":"403277a91b658dc9625db2afd0958575cae850c16a2056939bf5fdd1d519a143"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:54:21.453595Z","signature_b64":"ItqCUI14ZOKV2BgGs2yYkEpPceMt5KicQdSijCgIu3FmhhzWLsoKZiG9C45DnNGYtbGezSTjXcWtJqCaRY7gDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a0b5982d79bc78e53302cbdaf870c08bb18a09d91359f44e0940c4e4b750e573","last_reissued_at":"2026-05-18T00:54:21.453156Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:54:21.453156Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The Matrix Exponential Distribution - A Tool for Wireless System Performance Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Lars K. Rasmussen, Mikael Skoglund, Peter Larsson","submitted_at":"2016-12-20T18:56:30Z","abstract_excerpt":"In [1], we introduced a new, matrix algebraic, performance analysis framework for wireless systems with fading channels based on the matrix exponential distribution. The main idea was to use the compact, powerful, and easy-to-use, matrix exponential (ME)-distribution for i) modeling the unprocessed channel signal to noise ratio (SNR), ii) exploiting the closure property of the ME-distribution for SNR processing operations to give the effective channel random variable (r.v.) on ME-distribution form, and then to iii) express the performance measure in a closed-form based on ME-distribution matri"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1612.06809","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":"1612.06809","created_at":"2026-05-18T00:54:21.453216+00:00"},{"alias_kind":"arxiv_version","alias_value":"1612.06809v1","created_at":"2026-05-18T00:54:21.453216+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1612.06809","created_at":"2026-05-18T00:54:21.453216+00:00"},{"alias_kind":"pith_short_12","alias_value":"UC2ZQLLZXR4O","created_at":"2026-05-18T12:30:46.583412+00:00"},{"alias_kind":"pith_short_16","alias_value":"UC2ZQLLZXR4OKMYC","created_at":"2026-05-18T12:30:46.583412+00:00"},{"alias_kind":"pith_short_8","alias_value":"UC2ZQLLZ","created_at":"2026-05-18T12:30:46.583412+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/UC2ZQLLZXR4OKMYCZPNPQ4GARO","json":"https://pith.science/pith/UC2ZQLLZXR4OKMYCZPNPQ4GARO.json","graph_json":"https://pith.science/api/pith-number/UC2ZQLLZXR4OKMYCZPNPQ4GARO/graph.json","events_json":"https://pith.science/api/pith-number/UC2ZQLLZXR4OKMYCZPNPQ4GARO/events.json","paper":"https://pith.science/paper/UC2ZQLLZ"},"agent_actions":{"view_html":"https://pith.science/pith/UC2ZQLLZXR4OKMYCZPNPQ4GARO","download_json":"https://pith.science/pith/UC2ZQLLZXR4OKMYCZPNPQ4GARO.json","view_paper":"https://pith.science/paper/UC2ZQLLZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1612.06809&json=true","fetch_graph":"https://pith.science/api/pith-number/UC2ZQLLZXR4OKMYCZPNPQ4GARO/graph.json","fetch_events":"https://pith.science/api/pith-number/UC2ZQLLZXR4OKMYCZPNPQ4GARO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UC2ZQLLZXR4OKMYCZPNPQ4GARO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UC2ZQLLZXR4OKMYCZPNPQ4GARO/action/storage_attestation","attest_author":"https://pith.science/pith/UC2ZQLLZXR4OKMYCZPNPQ4GARO/action/author_attestation","sign_citation":"https://pith.science/pith/UC2ZQLLZXR4OKMYCZPNPQ4GARO/action/citation_signature","submit_replication":"https://pith.science/pith/UC2ZQLLZXR4OKMYCZPNPQ4GARO/action/replication_record"}},"created_at":"2026-05-18T00:54:21.453216+00:00","updated_at":"2026-05-18T00:54:21.453216+00:00"}