{"id":"W1662081346","doi":"10.1109/tsp.2015.2460223","title":"Linear Beamformer Design for Interference Alignment via Rank Minimization","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Matrix norm; Telecommunications link; Interference alignment; Algorithm; Minification; Rank (graph theory); Low-rank approximation; Interference (communication); Matrix (chemical analysis); Computer science; Mathematics; Norm (philosophy); Mathematical optimization; Beamforming; Telecommunications; MIMO; Combinatorics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001724965,0.0002055955,0.0001843255,0.0001477508,0.0001312402,0.00004667579,0.0001029048,0.0001036472,0.00001801002],"category_scores_gemma":[0.000004471121,0.0002112339,0.00005190136,0.0002395103,0.00002589551,0.0005054901,4.980154e-7,0.0001234224,0.0000220538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002060906,"about_ca_system_score_gemma":0.00005760176,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000232413,"about_ca_topic_score_gemma":0.000002456977,"domain_scores_codex":[0.9990101,0.00002713299,0.0003372293,0.0002202731,0.0001612455,0.0002440042],"domain_scores_gemma":[0.9994573,0.00006285208,0.00006385685,0.000114011,0.0001860139,0.0001159678],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009140963,0.0000360764,4.518722e-7,0.00008870084,0.00001926614,3.466932e-7,0.0005438194,0.9457121,0.006744799,0.000001533899,0.00008653084,0.04667494],"study_design_scores_gemma":[0.0006640868,0.0001397328,1.475844e-7,0.0001238755,0.00003586593,0.000007275079,0.0001420978,0.8648136,0.1334779,0.0001386984,0.000242018,0.0002146927],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0001423606,0.0001160973,0.9982648,0.00002216468,0.0003532187,0.0005647143,0.00001193148,0.0003646198,0.0001601156],"genre_scores_gemma":[0.8338338,0.00000913287,0.1656396,0.00003045387,0.00007514259,0.0002022097,0.000009123529,0.00006122884,0.0001393308],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8336914,"threshold_uncertainty_score":0.8613868,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04220756138108751,"score_gpt":0.2662238182560557,"score_spread":0.2240162568749682,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}