{"id":"W4407692709","doi":"10.1109/mecom61498.2024.10882105","title":"Machine Learning-Based Channel Estimation in Visible Light Communication with Signal-Dependent Noise","year":2024,"lang":"en","type":"article","venue":"","topic":"Optical Wireless Communication Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Computer science; Channel (broadcasting); Noise (video); SIGNAL (programming language); Visible light communication; Signal-to-noise ratio (imaging); Estimation; Artificial intelligence; Telecommunications; Physics; Engineering; Optoelectronics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007315226,0.0003072406,0.0003724124,0.0002559069,0.0002136688,0.0004989445,0.0004426957,0.0004996729,0.0003745194],"category_scores_gemma":[0.002837342,0.0001917194,0.0002019173,0.0002835671,0.0004519754,0.0005372848,0.000397115,0.0006548327,0.0001363844],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002921361,"about_ca_system_score_gemma":0.0006166429,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003383171,"about_ca_topic_score_gemma":0.002824207,"domain_scores_codex":[0.9997229,0.0001007625,0.00001380718,0.00005231719,0.00007089711,0.0000392833],"domain_scores_gemma":[0.9991207,0.0005972647,0.00007958629,0.00004400444,0.0001378597,0.00002072042],"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":[0.0001676943,0.00006473735,0.002871352,0.00009513324,0.00005847735,0.00009570298,0.00008422859,0.8386431,0.008936034,0.00768269,0.0005926238,0.1407082],"study_design_scores_gemma":[0.000001468138,0.00001204867,0.0002178256,0.000002961825,0.000002372126,0.00001029654,0.000004462334,0.9978671,0.001017708,0.0007629275,0.00009791946,0.000002850381],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05619495,0.0005131922,0.9416282,0.0001786983,0.00003504923,0.00001504084,0.00002067575,0.0001955405,0.00121864],"genre_scores_gemma":[0.9221899,0.0003803331,0.07519014,0.00007896698,0.00005971493,0.00003135233,0.00005481517,0.00002072055,0.001994099],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003383171,"threshold_uncertainty_score":0.006726921,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008699581218694564,"score_gpt":0.2230932925717298,"score_spread":0.2143937113530352,"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."}}