{"id":"W2918504904","doi":"10.1109/acssc.2018.8645239","title":"Widely Linear Multiuser Precoding for One-dimensional Signalling","year":2018,"lang":"en","type":"article","venue":"2018 52nd Asilomar Conference on Signals, Systems, and Computers","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Precoding; Zero-forcing precoding; Computer science; Transmission (telecommunications); Signal-to-noise ratio (imaging); Electronic engineering; Control theory (sociology); Mathematics; Algorithm; Telecommunications; MIMO; Engineering; Channel (broadcasting); Artificial intelligence","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":[0.000343315,0.0003723811,0.0004989842,0.0001811162,0.0002438772,0.0001593646,0.000211015,0.0001718939,0.00002153274],"category_scores_gemma":[0.00002676528,0.0003773697,0.0000750742,0.0001278761,0.0000973124,0.0002912104,0.0000450896,0.0001422557,0.00007220407],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008211395,"about_ca_system_score_gemma":0.00004490743,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002212332,"about_ca_topic_score_gemma":0.000009263649,"domain_scores_codex":[0.9981166,0.00007772299,0.0005925251,0.0005090925,0.0002518187,0.0004523025],"domain_scores_gemma":[0.9988029,0.0002199604,0.0001697849,0.0002699117,0.0003498774,0.0001875681],"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":[0.0003246385,0.0001704646,0.0002604631,0.001490274,0.0006768483,0.00001228576,0.001713649,0.8990053,0.04984907,0.01050872,0.011662,0.02432628],"study_design_scores_gemma":[0.0008295971,0.0003276363,0.00003056769,0.0009952312,0.00003025015,0.00001016063,0.0001668385,0.9925081,0.002563694,0.0001324491,0.001916712,0.0004887518],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01844132,0.0004346903,0.9763452,0.00004846895,0.002108808,0.0008721714,0.00006091332,0.0004909443,0.001197444],"genre_scores_gemma":[0.9596649,0.00004567264,0.0387869,0.00007752698,0.0009661981,0.00009788734,0.00004260516,0.0000833322,0.0002349321],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9412236,"threshold_uncertainty_score":0.9998678,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05397238420143019,"score_gpt":0.2606182221934885,"score_spread":0.2066458379920583,"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."}}