{"id":"W4399344039","doi":"10.1109/tvt.2024.3409192","title":"Hybrid Beamforming for mmWave Massive MIMO Systems Using Conditional Generative Adversarial Networks","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Antenna Design and Optimization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada); University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Beamforming; MIMO; Adversarial system; Computer science; Generative grammar; Electronic engineering; Engineering; Telecommunications; Artificial intelligence","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":[0.0007245571,0.0009521114,0.0005537255,0.0002157802,0.0002146803,0.0005445791,0.0007114896,0.0007863879,0.001861468],"category_scores_gemma":[0.001517081,0.0004015256,0.0004783766,0.0003081817,0.0008509827,0.0007413031,0.0009180103,0.00127213,0.0004136626],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005261738,"about_ca_system_score_gemma":0.000383519,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002188948,"about_ca_topic_score_gemma":0.003135658,"domain_scores_codex":[0.9996456,0.0001495912,0.000009625398,0.00006248251,0.00008250146,0.00005026034],"domain_scores_gemma":[0.9991905,0.0005870442,0.00006532714,0.00004345725,0.00008257211,0.00003115593],"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":[0.00003682432,0.00001031029,0.0002365163,0.00002031366,0.0000164367,0.00004088086,0.00001771421,0.9791514,0.001241897,0.007154216,0.0005429549,0.01153061],"study_design_scores_gemma":[0.000001861998,0.000009109306,0.00002871091,0.000001959122,0.000002005694,0.000008716724,0.00000183448,0.9977639,0.0002410846,0.001816929,0.0001216321,0.000002162082],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009264518,0.0001890636,0.9879463,0.0001902802,0.00002623675,0.00001745769,0.00004386728,0.0002085041,0.002113709],"genre_scores_gemma":[0.8905131,0.0004433425,0.1025408,0.000405481,0.00007135655,0.0001051109,0.000188151,0.00008003636,0.005652666],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002188948,"threshold_uncertainty_score":0.006227195,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01211602728381161,"score_gpt":0.2214938836085398,"score_spread":0.2093778563247282,"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."}}