{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:AEXZLOX3RVBM76VNAGI6MK3O35","short_pith_number":"pith:AEXZLOX3","schema_version":"1.0","canonical_sha256":"012f95bafb8d42cffaad0191e62b6edf6d4dfaab9a88002937c588d91832ff57","source":{"kind":"arxiv","id":"1507.04443","version":1},"attestation_state":"computed","paper":{"title":"Low-complexity near-optimal signal detection for uplink large-scale MIMO systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Linglong Dai, Xinyu Gao, Yongkui Ma, Zhaocheng Wang","submitted_at":"2015-07-16T03:52:12Z","abstract_excerpt":"Minimum mean square error (MMSE) signal detection algorithm is near- optimal for uplink multi-user large-scale multiple input multiple output (MIMO) systems, but involves matrix inversion with high complexity. In this letter, we firstly prove that the MMSE filtering matrix for large- scale MIMO is symmetric positive definite, based on which we propose a low-complexity near-optimal signal detection algorithm by exploiting the Richardson method to avoid the matrix inversion. The complexity can be reduced from O(K3) to O(K2), where K is the number of users. We also provide the convergence proof o"},"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":"1507.04443","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2015-07-16T03:52:12Z","cross_cats_sorted":["math.IT"],"title_canon_sha256":"d0410e6e63b85164df8045d482d4f9c6d8b9eba87c7f667d60bfc5b37ac5f304","abstract_canon_sha256":"cc71093e014086c52d08d8091ab79cb4d21024b55049d9676b63e40bf483c6e3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:36:47.356662Z","signature_b64":"kueyt+9SKTOYdoZU44WVrQoP31+FEw53sd6LuGDjOWrr1vVKYrTGiRTp/NUG9cdHpYhxrk4Ji5TFugKf7xiyDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"012f95bafb8d42cffaad0191e62b6edf6d4dfaab9a88002937c588d91832ff57","last_reissued_at":"2026-05-18T01:36:47.356041Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:36:47.356041Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Low-complexity near-optimal signal detection for uplink large-scale MIMO systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Linglong Dai, Xinyu Gao, Yongkui Ma, Zhaocheng Wang","submitted_at":"2015-07-16T03:52:12Z","abstract_excerpt":"Minimum mean square error (MMSE) signal detection algorithm is near- optimal for uplink multi-user large-scale multiple input multiple output (MIMO) systems, but involves matrix inversion with high complexity. In this letter, we firstly prove that the MMSE filtering matrix for large- scale MIMO is symmetric positive definite, based on which we propose a low-complexity near-optimal signal detection algorithm by exploiting the Richardson method to avoid the matrix inversion. The complexity can be reduced from O(K3) to O(K2), where K is the number of users. We also provide the convergence proof o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1507.04443","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":"1507.04443","created_at":"2026-05-18T01:36:47.356154+00:00"},{"alias_kind":"arxiv_version","alias_value":"1507.04443v1","created_at":"2026-05-18T01:36:47.356154+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1507.04443","created_at":"2026-05-18T01:36:47.356154+00:00"},{"alias_kind":"pith_short_12","alias_value":"AEXZLOX3RVBM","created_at":"2026-05-18T12:29:10.953037+00:00"},{"alias_kind":"pith_short_16","alias_value":"AEXZLOX3RVBM76VN","created_at":"2026-05-18T12:29:10.953037+00:00"},{"alias_kind":"pith_short_8","alias_value":"AEXZLOX3","created_at":"2026-05-18T12:29:10.953037+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/AEXZLOX3RVBM76VNAGI6MK3O35","json":"https://pith.science/pith/AEXZLOX3RVBM76VNAGI6MK3O35.json","graph_json":"https://pith.science/api/pith-number/AEXZLOX3RVBM76VNAGI6MK3O35/graph.json","events_json":"https://pith.science/api/pith-number/AEXZLOX3RVBM76VNAGI6MK3O35/events.json","paper":"https://pith.science/paper/AEXZLOX3"},"agent_actions":{"view_html":"https://pith.science/pith/AEXZLOX3RVBM76VNAGI6MK3O35","download_json":"https://pith.science/pith/AEXZLOX3RVBM76VNAGI6MK3O35.json","view_paper":"https://pith.science/paper/AEXZLOX3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1507.04443&json=true","fetch_graph":"https://pith.science/api/pith-number/AEXZLOX3RVBM76VNAGI6MK3O35/graph.json","fetch_events":"https://pith.science/api/pith-number/AEXZLOX3RVBM76VNAGI6MK3O35/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AEXZLOX3RVBM76VNAGI6MK3O35/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AEXZLOX3RVBM76VNAGI6MK3O35/action/storage_attestation","attest_author":"https://pith.science/pith/AEXZLOX3RVBM76VNAGI6MK3O35/action/author_attestation","sign_citation":"https://pith.science/pith/AEXZLOX3RVBM76VNAGI6MK3O35/action/citation_signature","submit_replication":"https://pith.science/pith/AEXZLOX3RVBM76VNAGI6MK3O35/action/replication_record"}},"created_at":"2026-05-18T01:36:47.356154+00:00","updated_at":"2026-05-18T01:36:47.356154+00:00"}