{"id":"W4383220260","doi":"10.1109/twc.2023.3290141","title":"Robust and Feasible QoS-Aware mmWave Massive MIMO Hybrid Beamforming","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Millimeter-Wave Propagation and Modeling","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Baseband; Beamforming; Computer science; MIMO; Channel (broadcasting); Quality of service; Channel state information; Mathematical optimization; Algorithm; Wireless; Computer network; Telecommunications; Bandwidth (computing); Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008343017,0.001208282,0.0007568835,0.0002949099,0.0003329663,0.001084998,0.0007483396,0.0007436862,0.001734515],"category_scores_gemma":[0.002104409,0.0004605686,0.0005758615,0.0004954811,0.0006793044,0.001087218,0.001363098,0.001003141,0.0005760591],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003719245,"about_ca_system_score_gemma":0.0006981182,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006432625,"about_ca_topic_score_gemma":0.0009935768,"domain_scores_codex":[0.9993815,0.0001646291,0.0000266796,0.00008175871,0.0002596944,0.000085698],"domain_scores_gemma":[0.9991515,0.0004211758,0.0001137775,0.0001075617,0.0001652196,0.00004078924],"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":[0.0001849112,0.00005033716,0.0006072438,0.0001225527,0.00005035339,0.0001358303,0.00009386381,0.8341585,0.03028069,0.05997103,0.002066194,0.07227838],"study_design_scores_gemma":[0.00001433595,0.0000411985,0.00008859871,0.000009341683,0.000005521868,0.00004686329,0.00001355087,0.9863912,0.00411619,0.008656383,0.0006057413,0.00001104091],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005574562,0.00005600146,0.9924477,0.00007980262,0.0000151518,0.00001457511,0.0000391527,0.0001172874,0.001655811],"genre_scores_gemma":[0.5319652,0.000300861,0.4635082,0.0002656725,0.00008102924,0.0001852414,0.0002531714,0.00006792986,0.003372648],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001734515,"threshold_uncertainty_score":0.005802572,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06159310221862827,"score_gpt":0.2564318784782488,"score_spread":0.1948387762596206,"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."}}