{"id":"W4237277773","doi":"10.36227/techrxiv.14331392.v1","title":"Visible Light Communication with Input-Dependent Noise: Channel Estimation, Optimal Receiver Design and Performance Analysis","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Optical Wireless Communication Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Estimator; Fading; Bit error rate; Channel (broadcasting); Cramér–Rao bound; Keying; Algorithm; Transmission (telecommunications); Visible light communication; Upper and lower bounds; Noise (video); Computer science; Signal-to-noise ratio (imaging); Mathematics; Telecommunications; Electronic engineering; Statistics; Physics; Engineering; Optics; Artificial intelligence","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":[0.001471988,0.001143565,0.001087057,0.0004461658,0.0003578548,0.0013625,0.000640266,0.001511211,0.001224885],"category_scores_gemma":[0.00428086,0.0004786298,0.0003790202,0.0007396646,0.001189869,0.001221526,0.000918386,0.0009921101,0.0006717969],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009576616,"about_ca_system_score_gemma":0.001400392,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00207522,"about_ca_topic_score_gemma":0.001500348,"domain_scores_codex":[0.9981717,0.0006169802,0.00006281391,0.0002713115,0.000698595,0.0001785711],"domain_scores_gemma":[0.9983568,0.0009453755,0.0001969563,0.0001161764,0.0003568981,0.00002778254],"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":[0.0002989483,0.0001033515,0.001721918,0.0006587167,0.0001292402,0.000331507,0.0002153547,0.772527,0.03899569,0.09704863,0.001854126,0.08611547],"study_design_scores_gemma":[0.000009182075,0.00007677645,0.0003329053,0.00003406006,0.00002378006,0.0001807951,0.00002827353,0.9792627,0.008360196,0.01042238,0.00124399,0.00002496154],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01144538,0.001764348,0.98344,0.0002628054,0.00003725767,0.00002619026,0.00004271898,0.0001155736,0.002865643],"genre_scores_gemma":[0.7649983,0.005916733,0.2226423,0.0002712423,0.000305102,0.0001892387,0.0002416366,0.0001199347,0.005315472],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00207522,"threshold_uncertainty_score":0.007784665,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01516952715082841,"score_gpt":0.2262292404225792,"score_spread":0.2110597132717507,"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."}}