{"id":"W4381785968","doi":"10.1109/jsac.2023.3288269","title":"Super-Wideband Massive MIMO","year":2023,"lang":"en","type":"article","venue":"IEEE Journal on Selected Areas in Communications","topic":"Antenna Design and Analysis","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Futurewei Technologies; National Science Foundation","keywords":"MIMO; Computer science; Wideband; 3G MIMO; Bandwidth (computing); Electronic engineering; Antenna noise temperature; Antenna array; Antenna (radio); Topology (electrical circuits); Telecommunications; Omnidirectional antenna; Antenna efficiency; Electrical engineering; Channel (broadcasting); Engineering","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":[0.0002867639,0.0007465791,0.000536622,0.0003113743,0.0002732766,0.001081584,0.001022306,0.001084146,0.002733476],"category_scores_gemma":[0.0007236951,0.0003207771,0.000564474,0.0004280338,0.0007384982,0.001299812,0.001021766,0.001070431,0.001161709],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005760688,"about_ca_system_score_gemma":0.0002768359,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008396451,"about_ca_topic_score_gemma":0.0008252521,"domain_scores_codex":[0.9996747,0.00008897472,0.000009441158,0.00005848939,0.0001188941,0.00004954877],"domain_scores_gemma":[0.9997384,0.00007985734,0.00004671102,0.00005679407,0.00005344562,0.00002487831],"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":[0.00005025157,0.00003544326,0.000520821,0.0001141198,0.0000417936,0.0003691301,0.0001488016,0.5489791,0.01279818,0.4214334,0.003483807,0.01202516],"study_design_scores_gemma":[0.000006730731,0.00004753038,0.000164879,0.00001251317,0.00001236122,0.000163483,0.0000312488,0.946401,0.0007171037,0.04813416,0.004295429,0.00001350536],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0171892,0.0006742691,0.9515628,0.0004792859,0.0001563778,0.00002762979,0.0001786719,0.0002210403,0.02951088],"genre_scores_gemma":[0.888922,0.001868167,0.079117,0.0008832454,0.0003885742,0.0001896499,0.000213705,0.00009469204,0.0283229],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002733476,"threshold_uncertainty_score":0.009144366,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03266448274842102,"score_gpt":0.2721250114076663,"score_spread":0.2394605286592452,"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."}}