{"id":"W2971344689","doi":"10.1109/globalsip45357.2019.8969563","title":"A Worst-Case Performance Optimization Based Design Approach to Robust Symbol-Level Precoding for Downlink MU-MIMO","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Telecommunications link; Precoding; Mathematical optimization; Coordinate descent; Computer science; MIMO; Optimization problem; Transmitter power output; Control theory (sociology); Convex optimization; Mathematics; Algorithm; Transmitter; Regular polygon; Channel (broadcasting); Telecommunications","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005986647,0.000695759,0.0007026629,0.0004982144,0.0001428354,0.0001853182,0.0004134294,0.0006275906,0.00003348834],"category_scores_gemma":[0.0001244262,0.0007435646,0.0001662232,0.0003539282,0.00001546929,0.0003568301,0.0001790404,0.0004694843,0.00003487862],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005483947,"about_ca_system_score_gemma":0.0001291413,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001252598,"about_ca_topic_score_gemma":0.000005067028,"domain_scores_codex":[0.9974161,0.00005656345,0.0008061942,0.0008978657,0.0002195851,0.0006037317],"domain_scores_gemma":[0.9981257,0.0002067998,0.0002006488,0.0009510478,0.0003220239,0.0001937975],"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":[0.00004892567,0.00004241369,0.00002191649,0.002253708,0.00006688889,0.000002534736,0.0002255825,0.9957135,0.00002969599,0.00004211045,0.0005734047,0.0009792743],"study_design_scores_gemma":[0.0007168101,0.00005299939,0.000004029098,0.0004700551,0.00007538797,0.00003845845,0.00009333083,0.9969693,0.0006307254,0.00001088927,0.00004870884,0.0008892498],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001385599,0.00009182596,0.9854568,0.00001923932,0.001255834,0.00654937,0.0001370278,0.001099993,0.005251388],"genre_scores_gemma":[0.2034931,0.00002792425,0.7930536,0.00004451812,0.0002182521,0.001773034,0.0005466437,0.0002327605,0.0006101406],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2033546,"threshold_uncertainty_score":0.9995015,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08031069240390237,"score_gpt":0.2430270052333734,"score_spread":0.162716312829471,"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."}}