{"id":"W4409641268","doi":"10.1109/lwc.2025.3563156","title":"A Novel Machine Learning Algorithm With Mathematical Modeling for Channel Estimation in VLC Systems","year":2025,"lang":"en","type":"article","venue":"IEEE Wireless Communications Letters","topic":"Semiconductor Lasers and Optical Devices","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Channel (broadcasting); Algorithm; Artificial intelligence; Machine learning; Telecommunications","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":[0.001276114,0.0007029022,0.0008106843,0.0006503217,0.0004302695,0.0008402458,0.001103084,0.001005413,0.001220675],"category_scores_gemma":[0.004977214,0.0003966623,0.00054549,0.0008869788,0.0007094686,0.001711475,0.0009099282,0.00169603,0.000654803],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006398958,"about_ca_system_score_gemma":0.00107198,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002412806,"about_ca_topic_score_gemma":0.001885208,"domain_scores_codex":[0.9992365,0.0002593466,0.00004173644,0.0001266347,0.0002836094,0.00005220514],"domain_scores_gemma":[0.9985937,0.0008188587,0.0001316701,0.0001215185,0.0003089034,0.00002533279],"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":[0.00005583825,0.00006166671,0.0006864296,0.0001095023,0.00005725391,0.00006857567,0.00006688753,0.7731406,0.005024525,0.04762578,0.00170552,0.1713974],"study_design_scores_gemma":[0.000001570069,0.00001005542,0.00003331334,0.000003098665,0.000001902502,0.0000133506,0.000001561493,0.9967443,0.0004037665,0.002367534,0.0004155929,0.000003987029],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001019433,0.00009917941,0.9984316,0.00005332432,0.00001864881,0.000008169264,0.000007501101,0.00008680911,0.0002753565],"genre_scores_gemma":[0.2928736,0.0007560641,0.701995,0.0002718083,0.0002115162,0.0002265803,0.0001581306,0.0001148076,0.003392554],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002412806,"threshold_uncertainty_score":0.006748855,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02353512704203958,"score_gpt":0.2511700265097941,"score_spread":0.2276348994677546,"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."}}