{"id":"W4402831789","doi":"10.1109/tcomm.2024.3468211","title":"Linearization of Fully-Connected Hybrid Beamforming Transmitters Using Analytical Multi-Input Models for Millimeter-Wave Communications","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Millimeter-Wave Propagation and Modeling","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Science Basic Research Program of Shaanxi Province; Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Beamforming; Linearization; Extremely high frequency; Transmitter; Electronic engineering; Computer science; Telecommunications; Engineering; Physics; Nonlinear system","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003014421,0.0002800347,0.0003282961,0.0005893898,0.0003990578,0.00009253603,0.0006672336,0.0001329454,0.00002869366],"category_scores_gemma":[0.00001222129,0.0003123191,0.0003301611,0.0006098552,0.0001925666,0.0004309812,0.00001033008,0.0004967317,0.000008874703],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001902337,"about_ca_system_score_gemma":0.00009324053,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004295338,"about_ca_topic_score_gemma":0.00007046309,"domain_scores_codex":[0.998317,0.0001083286,0.000806953,0.0002774902,0.0001900557,0.0003001477],"domain_scores_gemma":[0.9971344,0.0006092385,0.0000657216,0.001826734,0.0002417668,0.0001221009],"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":[0.00001736445,0.0002583695,4.192537e-7,0.0002309797,0.0003639883,4.885634e-7,0.001255558,0.8964329,0.0727782,0.0005729938,0.00002781958,0.02806092],"study_design_scores_gemma":[0.0004189795,0.00004809123,9.344905e-7,0.0002337325,0.0003044681,0.000013962,0.0001272038,0.9482017,0.04930136,0.0003717159,0.0006914248,0.0002864622],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007768224,0.0008803204,0.9885396,0.0004654941,0.0003069877,0.0007427038,0.0005187598,0.0005376727,0.0002402348],"genre_scores_gemma":[0.8007132,0.001105442,0.1976567,0.00005168763,0.00001847814,0.0001663224,0.0001534128,0.00008480714,0.00004999437],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7929449,"threshold_uncertainty_score":0.9999329,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1345888325002936,"score_gpt":0.3085188945485586,"score_spread":0.173930062048265,"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."}}