{"id":"W4405945858","doi":"10.18280/mmep.111202","title":"Deep Learning-Based Channel Estimation and Dynamic IRS Assignment for Optimized Beamforming in IRS-Aided MC-NOMA Systems","year":2024,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Noma; Beamforming; Channel (broadcasting); Computer science; Telecommunications","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003034682,0.0002271217,0.0003086732,0.0002435335,0.0000597878,0.0001258597,0.0001053906,0.0001390407,7.625463e-7],"category_scores_gemma":[0.00007299804,0.0002224979,0.00003555528,0.0001535057,0.00002928303,0.0001581667,0.00003591795,0.0003013115,0.000001859702],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001322188,"about_ca_system_score_gemma":0.000005892447,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002090608,"about_ca_topic_score_gemma":3.628687e-7,"domain_scores_codex":[0.9989852,0.000009062676,0.000390667,0.0002207751,0.0001040149,0.000290326],"domain_scores_gemma":[0.9992449,0.0004742004,0.00002720513,0.0001824853,0.00001622313,0.0000549737],"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":[0.000002762718,0.00001019174,3.738079e-7,0.003240289,0.00001963626,9.574354e-7,0.000353833,0.9899423,0.0002636999,0.003094116,6.597826e-7,0.003071182],"study_design_scores_gemma":[0.0003022481,0.00003309723,4.709767e-7,0.001372968,0.00001452856,0.000007391523,0.0001017989,0.9910817,0.0001736437,0.006622928,0.00004640041,0.0002427823],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009532951,0.004621073,0.9838284,0.0000635472,0.00007120849,0.0005128367,0.000002148861,0.001345627,0.0000221889],"genre_scores_gemma":[0.801002,0.0003433083,0.1981214,0.000001037085,0.000005791385,0.0004397305,0.00001073518,0.000061742,0.00001424668],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7914691,"threshold_uncertainty_score":0.9073203,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01551398066983463,"score_gpt":0.2221828160879592,"score_spread":0.2066688354181245,"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."}}