{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:R3ZGPLOCLUIKABCL4OP2MRP2K6","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"30e7e6cdd550a9b444a9c0c8966cb3d5f2eafa086c6b449e31275e3f8fb23dcb","cross_cats_sorted":["cs.IT","math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2018-04-17T15:04:23Z","title_canon_sha256":"62ba46d6129c1404f624076bc622887cde26857190184365e355b65daebdc58c"},"schema_version":"1.0","source":{"id":"1804.06737","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1804.06737","created_at":"2026-05-18T00:18:05Z"},{"alias_kind":"arxiv_version","alias_value":"1804.06737v1","created_at":"2026-05-18T00:18:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1804.06737","created_at":"2026-05-18T00:18:05Z"},{"alias_kind":"pith_short_12","alias_value":"R3ZGPLOCLUIK","created_at":"2026-05-18T12:32:50Z"},{"alias_kind":"pith_short_16","alias_value":"R3ZGPLOCLUIKABCL","created_at":"2026-05-18T12:32:50Z"},{"alias_kind":"pith_short_8","alias_value":"R3ZGPLOC","created_at":"2026-05-18T12:32:50Z"}],"graph_snapshots":[{"event_id":"sha256:3506f107e6e9e58391e15ac8955ba5a90f751d75703640219b81fe78e8c90e1b","target":"graph","created_at":"2026-05-18T00:18:05Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"For massive multiple-input multiple-output (MIMO) systems, linear minimum mean-square error (MMSE) detection has been shown to achieve near-optimal performance but suffers from excessively high complexity due to the large-scale matrix inversion. Being matrix inversion free, detection algorithms based on the Gauss-Seidel (GS) method have been proved more efficient than conventional Neumann series expansion (NSE) based ones. In this paper, an efficient GS-based soft-output data detector for massive MIMO and a corresponding VLSI architecture are proposed. To accelerate the convergence of the GS m","authors_text":"2, (2) Quantum Information Center, 3), (3) National Mobile Communications Research Laboratory, (4) School of Electrical, China, Christoph Studer (4), Chuan Zhang (1, Computer Engineering, Cornell University, Signal-processing (LEADS), Southeast University, USA), Xiaohu You (3) ((1) Lab of Efficient Architectures for Digital-communication, Zaichen Zhang (2, Zhizhen Wu (1","cross_cats":["cs.IT","math.IT"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2018-04-17T15:04:23Z","title":"Efficient Soft-Output Gauss-Seidel Data Detector for Massive MIMO Systems"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1804.06737","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:e5ebb71eeef11d68418a8e32c43c62a5ad7f687f058e367f4bc62501045e95ac","target":"record","created_at":"2026-05-18T00:18:05Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"30e7e6cdd550a9b444a9c0c8966cb3d5f2eafa086c6b449e31275e3f8fb23dcb","cross_cats_sorted":["cs.IT","math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2018-04-17T15:04:23Z","title_canon_sha256":"62ba46d6129c1404f624076bc622887cde26857190184365e355b65daebdc58c"},"schema_version":"1.0","source":{"id":"1804.06737","kind":"arxiv","version":1}},"canonical_sha256":"8ef267adc25d10a0044be39fa645fa57bcf1066368d1057e6151b750462ce30f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8ef267adc25d10a0044be39fa645fa57bcf1066368d1057e6151b750462ce30f","first_computed_at":"2026-05-18T00:18:05.874128Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:18:05.874128Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"XXrofSZxB+QTgjdVs3/NJSZWHgwECVwA390IHmmbzdZ7rA2ZVEXrx2wObuQvdZ9XYNmHrTetE7bMl90x41V4Bw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:18:05.874842Z","signed_message":"canonical_sha256_bytes"},"source_id":"1804.06737","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e5ebb71eeef11d68418a8e32c43c62a5ad7f687f058e367f4bc62501045e95ac","sha256:3506f107e6e9e58391e15ac8955ba5a90f751d75703640219b81fe78e8c90e1b"],"state_sha256":"632da14c474d00eacec0129b1bbdda4725ef306192da56773df5db7e8355506d"}