{"id":"W4415624518","doi":"10.1109/tcomm.2025.3626652","title":"Beamforming for Massive MIMO Aerial Communications: A Robust and Scalable DRL Approach","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Communications","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; École de Technologie Supérieure","funders":"","keywords":"Beamforming; Scalability; Robustness (evolution); Telecommunications link; Base station; MIMO; Channel state information; Overhead (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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000522417,0.0006654084,0.000567805,0.0002293663,0.0002272073,0.000480107,0.0008757932,0.0005751317,0.002008767],"category_scores_gemma":[0.0009823322,0.0003072986,0.0003250458,0.0002598565,0.0005506696,0.0008247669,0.0009481564,0.0009069744,0.0005267215],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004869957,"about_ca_system_score_gemma":0.0006702965,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003015589,"about_ca_topic_score_gemma":0.003775259,"domain_scores_codex":[0.9997619,0.0000596483,0.000008481319,0.00005153375,0.00008280163,0.0000356597],"domain_scores_gemma":[0.9996842,0.0001307301,0.00004766686,0.00003924781,0.00007433485,0.00002383224],"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":[0.00003732957,0.00002310073,0.0002903189,0.00004593271,0.00001988021,0.00006832737,0.00003149674,0.9239001,0.00507167,0.008817059,0.001207258,0.06048759],"study_design_scores_gemma":[0.000003591177,0.00001311654,0.00002949179,0.000002000282,0.000001884303,0.00001042404,0.00000389638,0.9979372,0.0004305617,0.001253838,0.0003117015,0.000002302399],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003912194,0.0001135335,0.993634,0.000115353,0.00002234895,0.00001540511,0.00002229475,0.0002666886,0.001898169],"genre_scores_gemma":[0.7589467,0.0002670073,0.2344915,0.0002666335,0.00008302124,0.0001012845,0.0001219477,0.00008891585,0.005632868],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003015589,"threshold_uncertainty_score":0.006720006,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03286685200953706,"score_gpt":0.2646647069552256,"score_spread":0.2317978549456886,"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."}}