{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005121926,0.0002909164,0.0003469707,0.0001331604,0.0005413248,0.00004390718,0.001475534,0.00008016375,0.0001514551],"category_scores_gemma":[0.00001154397,0.0003698618,0.0001454687,0.0005562211,0.0000662749,0.000134395,0.0002866565,0.000505975,0.00002503939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001756925,"about_ca_system_score_gemma":0.0000854022,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001570056,"about_ca_topic_score_gemma":0.000439126,"domain_scores_codex":[0.9979326,0.000136114,0.0007840452,0.0003675333,0.0003223421,0.0004574404],"domain_scores_gemma":[0.9983076,0.00007676157,0.0001275101,0.00132443,0.00003951823,0.0001241339],"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":[0.000150083,0.0007235794,0.002117256,0.0005151652,0.0003688358,0.0000191716,0.001538444,0.9019,0.01667607,0.01870181,0.001334736,0.05595487],"study_design_scores_gemma":[0.0005693439,0.00006112962,0.0003102747,0.00005119415,0.00004975444,0.00001889042,0.00123635,0.9655898,0.0004704452,0.0005348282,0.0306874,0.0004205585],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04791848,0.00130622,0.9438858,0.0002788086,0.0006764365,0.0011197,0.003041112,0.0003104641,0.001462951],"genre_scores_gemma":[0.9897598,0.0008504887,0.005500641,0.00005860175,0.00001126199,0.001118659,0.002583374,0.0000336776,0.00008349263],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9418413,"threshold_uncertainty_score":0.9998753,"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."}}