{"id":"W4403917955","doi":"10.1109/twc.2024.3485128","title":"Machine-Learning-Aided TDD Massive MIMO Downlink Transmission for High-Mobility Multi-Antenna Users With Partial Uplink Channel State Information","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada); University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Huawei Technologies","keywords":"Telecommunications link; Computer science; MIMO; Channel state information; Transmission (telecommunications); Channel (broadcasting); Precoding; Multi-user MIMO; Computer network; Antenna (radio); Wireless; Telecommunications; Electronic engineering; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004586811,0.0004525328,0.0004889087,0.0001295571,0.0002693964,0.000420237,0.0006036449,0.0004559787,0.001001763],"category_scores_gemma":[0.001655531,0.0002517243,0.0002671268,0.0002889826,0.0004450273,0.000621264,0.0006468694,0.0008251325,0.0003365071],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004617803,"about_ca_system_score_gemma":0.0008565285,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003954569,"about_ca_topic_score_gemma":0.005975989,"domain_scores_codex":[0.9997838,0.00006117841,0.000008326323,0.00004459908,0.00006037865,0.00004170239],"domain_scores_gemma":[0.9994773,0.000294918,0.0000527191,0.00005963633,0.00009056422,0.00002498786],"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":[0.0001151462,0.0000399593,0.0007143977,0.00004722252,0.000022345,0.00008848634,0.00004368131,0.9486341,0.004134301,0.003227714,0.001069377,0.04186319],"study_design_scores_gemma":[0.0000019212,0.00001226734,0.00005244,0.000001599254,0.000002172558,0.00001400386,0.000003511994,0.998657,0.0006868091,0.0004722988,0.00009408552,0.000001809426],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06334188,0.0004124508,0.9315517,0.0004572014,0.00009622888,0.00002479961,0.0001061579,0.0006208031,0.003388772],"genre_scores_gemma":[0.9500289,0.0001866509,0.04732941,0.0001552179,0.00003254643,0.00003716855,0.000111776,0.00002220913,0.002096188],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003954569,"threshold_uncertainty_score":0.007863104,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0171463223826948,"score_gpt":0.2496682288521534,"score_spread":0.2325219064694586,"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."}}