{"id":"W4390396728","doi":"10.1002/ett.4926","title":"Recurrent neural network and federated learning based channel estimation approach in mmWave massive MIMO systems","year":2023,"lang":"en","type":"article","venue":"Transactions on Emerging Telecommunications Technologies","topic":"Millimeter-Wave Propagation and Modeling","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Concordia University","funders":"","keywords":"Computer science; Frame (networking); Channel (broadcasting); Recurrent neural network; Telecommunications link; MIMO; Channel state information; Artificial neural network; Transmission (telecommunications); Artificial intelligence; Wireless; Machine learning; Deep learning; Real-time computing; Computer network; Telecommunications","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":[],"consensus_categories":[],"category_scores_codex":[0.0002965952,0.0001998596,0.000205768,0.0007168262,0.0005058729,0.00008685303,0.000231546,0.000159196,0.000003794417],"category_scores_gemma":[0.00004079387,0.000217515,0.00004302183,0.001334714,0.00006319406,0.0001442648,0.00001642743,0.0007322727,0.00001289534],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001018707,"about_ca_system_score_gemma":0.00001332018,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002060559,"about_ca_topic_score_gemma":0.00002042519,"domain_scores_codex":[0.9988725,0.0001000028,0.0003647956,0.0002275219,0.0001154846,0.0003197554],"domain_scores_gemma":[0.999292,0.0001564971,0.0000607507,0.000411998,0.0000507482,0.00002805464],"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":[0.000003942905,0.00002699764,0.00001589537,0.00005264522,0.00001938624,4.858742e-7,0.0001714316,0.9552542,0.0002267587,0.0000404233,0.00004893848,0.04413889],"study_design_scores_gemma":[0.0002350271,0.00003350166,0.00008294442,0.0001110006,0.00001452247,0.000002490719,0.001930268,0.9962677,0.0008641751,0.0001452638,0.0001034147,0.000209637],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04022771,0.0007948578,0.9522612,0.0009627044,0.0001522053,0.0004668575,0.000006445256,0.004841826,0.0002861844],"genre_scores_gemma":[0.9854159,0.001550358,0.01238259,0.00000790883,0.000005626047,0.0004834101,0.00008798428,0.00003954691,0.00002670165],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9451882,"threshold_uncertainty_score":0.8870004,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03301363439004193,"score_gpt":0.2529608355104361,"score_spread":0.2199472011203942,"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."}}