{"id":"W2908426266","doi":"10.1109/tvt.2018.2872881","title":"User Selection and Multiuser Widely Linear Precoding for One-Dimensional Signalling","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Precoding; Selection (genetic algorithm); Computer science; Zero-forcing precoding; Signalling; Computer network; Artificial intelligence; MIMO; Cell biology; Biology; Channel (broadcasting)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006570181,0.0005428926,0.0004730715,0.0002529713,0.0002634093,0.0006598136,0.0005129446,0.0005317643,0.001067433],"category_scores_gemma":[0.002639659,0.0002478448,0.0003173224,0.000604351,0.0008558929,0.0007520689,0.0007528859,0.0006573375,0.0003968951],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003799274,"about_ca_system_score_gemma":0.0004266389,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005398081,"about_ca_topic_score_gemma":0.0007847408,"domain_scores_codex":[0.9990754,0.0004795245,0.00002905196,0.000120514,0.0002173963,0.00007813732],"domain_scores_gemma":[0.998813,0.0007762015,0.0001102262,0.0001276265,0.0001415108,0.00003150418],"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":[0.0003607766,0.00007938607,0.001012359,0.0001942951,0.00009673366,0.0004526879,0.0004876506,0.5452235,0.05691527,0.2010416,0.002012193,0.1921234],"study_design_scores_gemma":[0.00001301352,0.00009167863,0.0001321199,0.00001320598,0.00001059695,0.0001391922,0.00002347923,0.9763209,0.004877227,0.01717192,0.001189864,0.00001666136],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01321656,0.0002978445,0.9846555,0.0001277291,0.00002387531,0.00001755191,0.00001433156,0.00006781799,0.001578857],"genre_scores_gemma":[0.7603841,0.0009061637,0.2341482,0.0002044011,0.0001183899,0.000133705,0.00006138639,0.00002365289,0.004019877],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001067433,"threshold_uncertainty_score":0.003570914,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01320454068015892,"score_gpt":0.2352358123733162,"score_spread":0.2220312716931573,"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."}}