{"id":"W4315629725","doi":"10.1109/globecom48099.2022.10001495","title":"6G Intelligent Distributed Uplink Beamforming for Transport System in Highly Dynamic Environments","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Millimeter-Wave Propagation and Modeling","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"National Natural Science Foundation of China","keywords":"Beamforming; Telecommunications link; Computer science; WSDMA; MIMO; Transmission (telecommunications); Wireless; Base station; Multiplexing; Orthogonal frequency-division multiplexing; Wireless network; Computer network; Channel state information; Real-time computing; Electronic engineering; Channel (broadcasting); Engineering; Telecommunications; Precoding","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":[0.0004101858,0.0007578384,0.0004300206,0.0002617952,0.0003972581,0.0005443852,0.0005704582,0.0006082052,0.003021622],"category_scores_gemma":[0.0005589803,0.0001964541,0.0002941262,0.0003580437,0.0003451346,0.001009428,0.0005962063,0.0007169871,0.001091306],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005331511,"about_ca_system_score_gemma":0.0005078483,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002142868,"about_ca_topic_score_gemma":0.004593177,"domain_scores_codex":[0.9997144,0.00008394723,0.00001181562,0.0000476264,0.00008714899,0.00005502995],"domain_scores_gemma":[0.9997945,0.000043664,0.00002058748,0.00002772402,0.00009463623,0.00001888369],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003714836,0.0001150976,0.00334671,0.0001819063,0.0001006862,0.0003261862,0.0001778696,0.3632584,0.08778557,0.0300447,0.01173762,0.5025539],"study_design_scores_gemma":[0.00002067805,0.0001357372,0.0004705261,0.00002001395,0.00002627281,0.0001291614,0.00005462767,0.9695292,0.01414377,0.007866979,0.007584121,0.00001889593],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01435998,0.0003957619,0.9781225,0.0004115695,0.0001137257,0.00002944315,0.00006875042,0.001289991,0.005208192],"genre_scores_gemma":[0.8312032,0.0007369372,0.1593923,0.000418659,0.0001282943,0.00008414486,0.0002342715,0.00009132009,0.007710901],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003021622,"threshold_uncertainty_score":0.01010835,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02977274784886347,"score_gpt":0.2560944657299564,"score_spread":0.2263217178810929,"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."}}