{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:GXRFBJ54ETIS7CYFP5X2JV3CNH","short_pith_number":"pith:GXRFBJ54","schema_version":"1.0","canonical_sha256":"35e250a7bc24d12f8b057f6fa4d76269cdb4a7b0ec37f2cabfa82b9f2c7f782e","source":{"kind":"arxiv","id":"1704.03219","version":1},"attestation_state":"computed","paper":{"title":"Error Vector Magnitude Analysis in Generalized Fading with Co-Channel Interference","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"K. Giridhar, Radha Krishna Ganti, Sheetal Kalyani, Sudharsan Parthasarathy, Suman Kumar","submitted_at":"2017-04-11T09:40:51Z","abstract_excerpt":"In this paper, we derive the data-aided Error Vector Magnitude (EVM) in an interference limited system when both the desired signal and interferers experience independent and non identically distributed $\\kappa$-$\\mu$ shadowed fading. Then it is analytically shown that the EVM is equal to the square root of number of interferers when the desired signal and interferers do not experience fading. Further, EVM is derived in the presence of interference and noise, when the desired signal experiences $\\kappa$-$\\mu$ shadowed fading and the interferers experience independent and identical Nakagami fad"},"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":"1704.03219","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2017-04-11T09:40:51Z","cross_cats_sorted":["math.IT"],"title_canon_sha256":"13efc29d392c2471b156b85cc3b44d3d5438863ee2b4e2e1c4557a15e5784331","abstract_canon_sha256":"80e5c62989f437bd7b4fd673291b5699dde7ab152d86e82f2417bc05d1e0300c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:46:33.150398Z","signature_b64":"/prgVAHh+WibssWyAhrTkhs81CF0aIsJbBtAVd8r0MYS18NU0vudSdkDOk1dpMGuQmgzH1IRyvh0rT+0H3CICg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"35e250a7bc24d12f8b057f6fa4d76269cdb4a7b0ec37f2cabfa82b9f2c7f782e","last_reissued_at":"2026-05-18T00:46:33.149827Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:46:33.149827Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Error Vector Magnitude Analysis in Generalized Fading with Co-Channel Interference","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"K. Giridhar, Radha Krishna Ganti, Sheetal Kalyani, Sudharsan Parthasarathy, Suman Kumar","submitted_at":"2017-04-11T09:40:51Z","abstract_excerpt":"In this paper, we derive the data-aided Error Vector Magnitude (EVM) in an interference limited system when both the desired signal and interferers experience independent and non identically distributed $\\kappa$-$\\mu$ shadowed fading. Then it is analytically shown that the EVM is equal to the square root of number of interferers when the desired signal and interferers do not experience fading. Further, EVM is derived in the presence of interference and noise, when the desired signal experiences $\\kappa$-$\\mu$ shadowed fading and the interferers experience independent and identical Nakagami fad"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1704.03219","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":"1704.03219","created_at":"2026-05-18T00:46:33.149923+00:00"},{"alias_kind":"arxiv_version","alias_value":"1704.03219v1","created_at":"2026-05-18T00:46:33.149923+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1704.03219","created_at":"2026-05-18T00:46:33.149923+00:00"},{"alias_kind":"pith_short_12","alias_value":"GXRFBJ54ETIS","created_at":"2026-05-18T12:31:18.294218+00:00"},{"alias_kind":"pith_short_16","alias_value":"GXRFBJ54ETIS7CYF","created_at":"2026-05-18T12:31:18.294218+00:00"},{"alias_kind":"pith_short_8","alias_value":"GXRFBJ54","created_at":"2026-05-18T12:31:18.294218+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/GXRFBJ54ETIS7CYFP5X2JV3CNH","json":"https://pith.science/pith/GXRFBJ54ETIS7CYFP5X2JV3CNH.json","graph_json":"https://pith.science/api/pith-number/GXRFBJ54ETIS7CYFP5X2JV3CNH/graph.json","events_json":"https://pith.science/api/pith-number/GXRFBJ54ETIS7CYFP5X2JV3CNH/events.json","paper":"https://pith.science/paper/GXRFBJ54"},"agent_actions":{"view_html":"https://pith.science/pith/GXRFBJ54ETIS7CYFP5X2JV3CNH","download_json":"https://pith.science/pith/GXRFBJ54ETIS7CYFP5X2JV3CNH.json","view_paper":"https://pith.science/paper/GXRFBJ54","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1704.03219&json=true","fetch_graph":"https://pith.science/api/pith-number/GXRFBJ54ETIS7CYFP5X2JV3CNH/graph.json","fetch_events":"https://pith.science/api/pith-number/GXRFBJ54ETIS7CYFP5X2JV3CNH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GXRFBJ54ETIS7CYFP5X2JV3CNH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GXRFBJ54ETIS7CYFP5X2JV3CNH/action/storage_attestation","attest_author":"https://pith.science/pith/GXRFBJ54ETIS7CYFP5X2JV3CNH/action/author_attestation","sign_citation":"https://pith.science/pith/GXRFBJ54ETIS7CYFP5X2JV3CNH/action/citation_signature","submit_replication":"https://pith.science/pith/GXRFBJ54ETIS7CYFP5X2JV3CNH/action/replication_record"}},"created_at":"2026-05-18T00:46:33.149923+00:00","updated_at":"2026-05-18T00:46:33.149923+00:00"}