{"id":"W4406803321","doi":"10.18280/mmep.120132","title":"Deep Learning-Based Pilot Allocation for Optimized Channel Estimation and Pilot Contamination Reduction in Massive MIMO-OFDM Systems","year":2025,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Reduction (mathematics); Contamination; Channel (broadcasting); Orthogonal frequency-division multiplexing; MIMO; Computer science; Real-time computing; Environmental science; Telecommunications; Mathematics; Biology","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":[0.0003697324,0.0002182599,0.0003117488,0.0002969701,0.00007224416,0.00008363585,0.00004672286,0.00009965378,5.450888e-7],"category_scores_gemma":[0.0001009891,0.0002373494,0.00002039172,0.0002041231,0.0000191817,0.0002079781,0.00001027908,0.0001610537,9.353847e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001306845,"about_ca_system_score_gemma":0.000008752759,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001004907,"about_ca_topic_score_gemma":9.170578e-7,"domain_scores_codex":[0.9989424,0.0000227269,0.0004632991,0.0002497092,0.00008848033,0.0002333362],"domain_scores_gemma":[0.9994983,0.000194612,0.00006311246,0.0001091573,0.0000821026,0.00005272503],"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":[0.00002455442,0.00003328593,0.000001271741,0.002844822,0.00001884283,1.79889e-7,0.0002821129,0.9912403,0.0007722684,0.004354325,0.000002049729,0.0004259847],"study_design_scores_gemma":[0.001135297,0.0001177915,0.00000299455,0.001061207,0.00003186741,0.000003442248,0.00007669639,0.9948865,0.0002891933,0.002175221,0.000008201964,0.0002116139],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005522711,0.0007583374,0.9919117,0.00004983466,0.000195204,0.001136169,0.000001456227,0.0003665047,0.00005805394],"genre_scores_gemma":[0.9062864,0.00008895277,0.09280016,0.000001939433,0.00002358925,0.0006516561,0.00003917818,0.00004571045,0.00006246036],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9007636,"threshold_uncertainty_score":0.9678828,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01735466183197369,"score_gpt":0.2209090656622975,"score_spread":0.2035544038303238,"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."}}