{"paper":{"title":"Baseband-Efficient WMMSE Precoding: From a Signal Weighting Cost Perspective","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Sparse row-sparse precoding architectures within the WMMSE framework reduce signal weighting operations in MU-MIMO while preserving near-optimal sum rate via a proven low-dimensional subspace property.","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Fan Xu, Lei Qiu, Mian Li, Qingjiang Shi, Shuai Gao, Xinzhi Ning, Ye Yang","submitted_at":"2026-05-18T13:18:30Z","abstract_excerpt":"For downlink transmission in massive multi-user multiple-input multiple-output (MU-MIMO) systems, conventional precoding research heavily focuses on reducing the computational complexity of precoding matrix design, while largely overlooking another critical bottleneck: the substantial signal weighting cost incurred by repeatedly applying the precoder to high-speed data streams. To address both challenges simultaneously, this paper proposes a novel sparse precoding framework tailored for fully-digital architectures. Within this framework, from the sum-rate maximization perspective, we design tw"},"claims":{"count":3,"items":[{"kind":"strongest_claim","text":"We rigorously prove, for the first time, that the optimal precoder under both sparse architectures strictly resides in a specific low-dimensional subspace determined by the channel matrices, thereby reducing the dimensionality of the optimization variables.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The assumption that the mixed-integer non-linear program remains tractable and that the alternating optimization with penalty-based MM converges to a solution whose sum-rate loss is negligible when the subspace reduction is applied; this is invoked in the transition from the MINLP formulation to the WMMSE alternating algorithm.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Sparse row-sparse precoding architectures within the WMMSE framework reduce signal weighting operations in MU-MIMO while preserving near-optimal sum rate via a proven low-dimensional subspace property.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"}],"snapshot_sha256":"fb7a01f8c253bbf3800e2828de88f480d966eac9f2fdb249c7540eecbe3a21ff"},"source":{"id":"2605.18368","kind":"arxiv","version":1},"verdict":{"id":"5a7dcdfd-e52e-4869-b4d2-dd0578346f73","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-20T00:03:35.875413Z","strongest_claim":"We rigorously prove, for the first time, that the optimal precoder under both sparse architectures strictly resides in a specific low-dimensional subspace determined by the channel matrices, thereby reducing the dimensionality of the optimization variables.","one_line_summary":"Sparse row-sparse precoding architectures within the WMMSE framework reduce signal weighting operations in MU-MIMO while preserving near-optimal sum rate via a proven low-dimensional subspace property.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The assumption that the mixed-integer non-linear program remains tractable and that the alternating optimization with penalty-based MM converges to a solution whose sum-rate loss is negligible when the subspace reduction is applied; this is invoked in the transition from the MINLP formulation to the WMMSE alternating algorithm.","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.18368/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"citation_quote_validity","ran_at":"2026-05-19T23:50:06.522975Z","status":"skipped","version":"0.1.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T23:33:30.010993Z","status":"skipped","version":"1.0.0","findings_count":0},{"name":"external_links","ran_at":"2026-05-19T23:31:49.568875Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T23:21:58.780537Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"74461bcf57aff95832c23a82ce9b4824062494fd5c4ca0644d69c7d29401294c"},"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"}