{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002236151,0.0001285276,0.0001878953,0.0005448678,0.0002237323,0.00007305077,0.0006457415,0.00007352413,0.0000638444],"category_scores_gemma":[0.0001355197,0.0001211922,0.00008282346,0.001976276,0.00004504091,0.0001226225,0.00002836379,0.0007249296,0.0002556408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001221842,"about_ca_system_score_gemma":0.00004296526,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009479763,"about_ca_topic_score_gemma":0.0001070963,"domain_scores_codex":[0.9990197,0.0001360771,0.0003252947,0.00008747551,0.0001670009,0.0002644007],"domain_scores_gemma":[0.9987152,0.0003893388,0.00004433738,0.0006037013,0.000144748,0.000102709],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001252551,0.001171255,0.04747317,0.0001008925,0.002121415,0.0005658866,0.008160794,0.1707276,0.4930042,0.005093871,0.2421659,0.02928983],"study_design_scores_gemma":[0.001613824,0.0001427013,0.03560073,0.0005821259,0.0001404045,0.0001834873,0.001388532,0.9243317,0.003083178,0.00322392,0.02877805,0.0009313411],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9179907,0.003661777,0.03225678,0.01586357,0.001295776,0.0005608284,0.00007566414,0.002367938,0.02592691],"genre_scores_gemma":[0.9940962,0.004802754,0.000475736,0.000096696,0.00007546902,0.00001609829,0.00002498725,0.00003074707,0.0003812966],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7536041,"threshold_uncertainty_score":0.4942074,"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."}}