{"id":"W4417170219","doi":"10.1109/tvt.2025.3642175","title":"Wavelet Convolution Enabled Distributed Machine Learning for Downlink Channel Estimation in RIS Assisted Communications","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Convolution (computer science); Channel (broadcasting); Overhead (engineering); Convolutional neural network; Feature (linguistics); Telecommunications link; Wavelet; Representation (politics)","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":["metaepi_narrow","research_integrity"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.0005437852,0.0006677011,0.0009043554,0.003097075,0.00105355,0.00006988004,0.001778371,0.001754566,0.00002315917],"category_scores_gemma":[0.0003760153,0.0008382156,0.0002811772,0.004449411,0.0007633108,0.0003286563,0.00004884069,0.003173568,0.0000340064],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001601401,"about_ca_system_score_gemma":0.0001547513,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009739083,"about_ca_topic_score_gemma":0.0003971366,"domain_scores_codex":[0.9964857,0.0002475992,0.001348252,0.0007465471,0.0002116071,0.0009603205],"domain_scores_gemma":[0.9955112,0.0007352653,0.0003503446,0.00294726,0.0003811396,0.00007475023],"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":[0.00009423935,0.0005957176,0.00003612214,0.000248223,0.0003259914,0.000003496379,0.00007618572,0.8152463,0.008403831,0.004759025,0.00002577632,0.1701851],"study_design_scores_gemma":[0.002598207,0.000207499,0.0001686634,0.0005920199,0.0001751465,0.00001371813,0.0005055809,0.9081789,0.07581989,0.00629599,0.004860547,0.0005838551],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005100806,0.004273008,0.9751427,0.009427774,0.000452956,0.0020872,0.0003782082,0.003050679,0.00008669994],"genre_scores_gemma":[0.9530647,0.005260806,0.03839797,0.00004545967,0.00000550922,0.002466857,0.0005000462,0.00009040653,0.0001682338],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9479639,"threshold_uncertainty_score":0.9995413,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01659485328187961,"score_gpt":0.2679133800285569,"score_spread":0.2513185267466773,"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."}}