{"id":"W2582328874","doi":"10.1109/twc.2017.2657745","title":"Sum-Rate Analysis for Massive MIMO Downlink With Joint Statistical Beamforming and User Scheduling","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":98,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Beamforming; Telecommunications link; Computer science; MIMO; Scheduling (production processes); Covariance matrix; Covariance; Mathematical optimization; Algorithm; Mathematics; Telecommunications; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003106044,0.002588035,0.00142024,0.001069259,0.0007150471,0.001690849,0.001375742,0.0008269406,0.003415756],"category_scores_gemma":[0.008285291,0.0006852141,0.001168896,0.001840135,0.00108341,0.001910927,0.001528551,0.001593137,0.001283782],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001783578,"about_ca_system_score_gemma":0.002209069,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002932472,"about_ca_topic_score_gemma":0.003052469,"domain_scores_codex":[0.9974214,0.0008541942,0.0001071353,0.000203764,0.001046034,0.0003675662],"domain_scores_gemma":[0.9961688,0.00214036,0.0003290084,0.0003144346,0.000966764,0.0000805886],"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":[0.00009778544,0.00006149626,0.0005163332,0.0002174737,0.00006264915,0.0001736895,0.00008759939,0.9177557,0.008013818,0.05031358,0.001636789,0.02106309],"study_design_scores_gemma":[0.000004214398,0.0000349564,0.0001075917,0.0000121296,0.00001320915,0.00007056716,0.00001991049,0.9924262,0.001516007,0.005427831,0.0003564175,0.00001102688],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01096858,0.001168943,0.9809403,0.000244657,0.0000630415,0.00006235895,0.0001292726,0.0002672475,0.006155711],"genre_scores_gemma":[0.8114638,0.004952052,0.1756619,0.000409869,0.0003613447,0.0004094151,0.0004325588,0.0003175182,0.005991506],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003415756,"threshold_uncertainty_score":0.0164265,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02883899567439352,"score_gpt":0.2742016945204447,"score_spread":0.2453626988460512,"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."}}