{"id":"W4383960546","doi":"10.1109/tvt.2023.3293988","title":"Energy Efficient Hybrid Precoding for Adaptive Partially-Connected mmWave Massive MIMO: A Decomposition-Based Approach","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Millimeter-Wave Propagation and Modeling","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Precoding; MIMO; Zero-forcing precoding; Computer science; Electronic engineering; Matrix decomposition; Antenna (radio); Efficient energy use; Spectral efficiency; Engineering; Telecommunications; Beamforming; Electrical engineering","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":[0.0001086195,0.0002588415,0.0002715695,0.0008750558,0.0002391241,0.00002546583,0.0001665928,0.0002386239,0.00002108466],"category_scores_gemma":[0.000008104224,0.0002802039,0.0001898381,0.0007077389,0.00006232782,0.00003963477,0.000001951056,0.0002732479,0.00003325483],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001389161,"about_ca_system_score_gemma":0.00003528292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007619479,"about_ca_topic_score_gemma":0.00001752716,"domain_scores_codex":[0.9986215,0.00003663161,0.0003296357,0.0004159515,0.0001531661,0.0004431564],"domain_scores_gemma":[0.9993134,0.0001205733,0.00005019569,0.0003137202,0.0001237022,0.00007838881],"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":[0.0000385849,0.00008236826,4.223466e-7,0.00003582343,0.0001144768,0.000008151608,0.00003366943,0.9185649,0.06701619,0.0001832056,0.00005332259,0.01386891],"study_design_scores_gemma":[0.0004679763,0.00008644899,4.218925e-7,0.00002657461,0.00004103074,0.000005926148,0.00006543948,0.5639607,0.4348516,0.0002064511,0.0001161905,0.0001712504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0883173,0.00004891379,0.9083469,0.0002400062,0.0003486401,0.0004780524,0.00008455415,0.002051453,0.00008419903],"genre_scores_gemma":[0.9860227,0.00002599795,0.01262613,0.00007416213,0.00002716697,0.001060458,0.00006347882,0.00007212087,0.00002777708],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8977054,"threshold_uncertainty_score":0.999965,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01933970506602967,"score_gpt":0.2301187584507022,"score_spread":0.2107790533846726,"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."}}