{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:VJRYAY7W77ONVUCYMP7LH75N3B","short_pith_number":"pith:VJRYAY7W","schema_version":"1.0","canonical_sha256":"aa638063f6ffdcdad05863feb3ffadd85a4aaba211fcbb115ed567e7231d4158","source":{"kind":"arxiv","id":"2108.04485","version":1},"attestation_state":"computed","paper":{"title":"Joint Pilot Design and Channel Estimation using Deep Residual Learning for Multi-Cell Massive MIMO under Hardware Impairments","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Byungju Lim, Joongheon Kim, Won Joon Yun, Young-Chai Ko","submitted_at":"2021-08-10T07:36:10Z","abstract_excerpt":"In multi-cell massive MIMO systems, channel estimation is deteriorated by pilot contamination and the effects of pilot contamination become more severe due to hardware impairments. In this paper, we propose a joint pilot design and channel estimation based on deep residual learning in order to mitigate the effects of pilot contamination under the consideration of hardware impairments. We first investigate a conventional linear minimum mean square error (LMMSE) based channel estimator to suppress the interference caused by pilot contamination. After that, a deep learning based pilot design is p"},"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":"2108.04485","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2021-08-10T07:36:10Z","cross_cats_sorted":[],"title_canon_sha256":"306ebacc75979cb5af74e2278e59856831801c49f8e6149fac2c7ea5821fc6d6","abstract_canon_sha256":"f5c7b146631897e103cd5f06489dad02d65c8953c2eb7da35c20f32b9614a43c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:04:31.040833Z","signature_b64":"Az9Q8z+ycBFDv4xgkM1oSUnegV+hbaCk554GIzNXGoDkhUWZM884DGfP7qwY68lLdhzjftmTWn1+GIcn/ketAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"aa638063f6ffdcdad05863feb3ffadd85a4aaba211fcbb115ed567e7231d4158","last_reissued_at":"2026-07-05T03:04:31.040446Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:04:31.040446Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Joint Pilot Design and Channel Estimation using Deep Residual Learning for Multi-Cell Massive MIMO under Hardware Impairments","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Byungju Lim, Joongheon Kim, Won Joon Yun, Young-Chai Ko","submitted_at":"2021-08-10T07:36:10Z","abstract_excerpt":"In multi-cell massive MIMO systems, channel estimation is deteriorated by pilot contamination and the effects of pilot contamination become more severe due to hardware impairments. In this paper, we propose a joint pilot design and channel estimation based on deep residual learning in order to mitigate the effects of pilot contamination under the consideration of hardware impairments. We first investigate a conventional linear minimum mean square error (LMMSE) based channel estimator to suppress the interference caused by pilot contamination. After that, a deep learning based pilot design is p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2108.04485","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/2108.04485/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":"2108.04485","created_at":"2026-07-05T03:04:31.040501+00:00"},{"alias_kind":"arxiv_version","alias_value":"2108.04485v1","created_at":"2026-07-05T03:04:31.040501+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2108.04485","created_at":"2026-07-05T03:04:31.040501+00:00"},{"alias_kind":"pith_short_12","alias_value":"VJRYAY7W77ON","created_at":"2026-07-05T03:04:31.040501+00:00"},{"alias_kind":"pith_short_16","alias_value":"VJRYAY7W77ONVUCY","created_at":"2026-07-05T03:04:31.040501+00:00"},{"alias_kind":"pith_short_8","alias_value":"VJRYAY7W","created_at":"2026-07-05T03:04:31.040501+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/VJRYAY7W77ONVUCYMP7LH75N3B","json":"https://pith.science/pith/VJRYAY7W77ONVUCYMP7LH75N3B.json","graph_json":"https://pith.science/api/pith-number/VJRYAY7W77ONVUCYMP7LH75N3B/graph.json","events_json":"https://pith.science/api/pith-number/VJRYAY7W77ONVUCYMP7LH75N3B/events.json","paper":"https://pith.science/paper/VJRYAY7W"},"agent_actions":{"view_html":"https://pith.science/pith/VJRYAY7W77ONVUCYMP7LH75N3B","download_json":"https://pith.science/pith/VJRYAY7W77ONVUCYMP7LH75N3B.json","view_paper":"https://pith.science/paper/VJRYAY7W","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2108.04485&json=true","fetch_graph":"https://pith.science/api/pith-number/VJRYAY7W77ONVUCYMP7LH75N3B/graph.json","fetch_events":"https://pith.science/api/pith-number/VJRYAY7W77ONVUCYMP7LH75N3B/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VJRYAY7W77ONVUCYMP7LH75N3B/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VJRYAY7W77ONVUCYMP7LH75N3B/action/storage_attestation","attest_author":"https://pith.science/pith/VJRYAY7W77ONVUCYMP7LH75N3B/action/author_attestation","sign_citation":"https://pith.science/pith/VJRYAY7W77ONVUCYMP7LH75N3B/action/citation_signature","submit_replication":"https://pith.science/pith/VJRYAY7W77ONVUCYMP7LH75N3B/action/replication_record"}},"created_at":"2026-07-05T03:04:31.040501+00:00","updated_at":"2026-07-05T03:04:31.040501+00:00"}