{"id":"W4226342350","doi":"10.1109/tvt.2022.3163078","title":"Optimal Channel Selection in Hybrid RF/VLC Networks: A Multi-Armed Bandit Approach","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Advanced Bandit Algorithms Research","field":"Decision Sciences","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University; University of Waterloo; Thunder Bay Regional Research Institute","funders":"","keywords":"Selection (genetic algorithm); Visible light communication; Computer science; Channel (broadcasting); Throughput; Wireless; Energy consumption; Radio frequency; Electronic engineering; Mathematical optimization; Convergence (economics); Multi-armed bandit; Engineering; Telecommunications; Mathematics; Machine learning; Electrical engineering","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"],"consensus_categories":[],"category_scores_codex":[0.001753693,0.00031461,0.0005147893,0.003184114,0.0008735736,0.00008227136,0.001290616,0.0002585239,0.000445333],"category_scores_gemma":[0.0001372586,0.0003034649,0.0002138897,0.005783956,0.0003435693,0.0002833534,0.0000334691,0.002290564,0.0001122504],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005589354,"about_ca_system_score_gemma":0.0001298183,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004120373,"about_ca_topic_score_gemma":0.00006972028,"domain_scores_codex":[0.9952892,0.0004054597,0.000734882,0.001210561,0.001479107,0.0008807565],"domain_scores_gemma":[0.9984466,0.0003345028,0.0001804802,0.0007030837,0.0002129704,0.0001223544],"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":[0.0001249482,0.000735994,0.00004510932,0.000002704382,0.00003457707,0.00009015449,0.0000775099,0.9740018,0.000541715,0.00001402774,0.0002037017,0.02412782],"study_design_scores_gemma":[0.001674267,0.000465901,0.0001224397,0.000006124972,0.0000135832,0.0003858799,0.00104015,0.9847127,0.007458192,0.0007720107,0.003019762,0.000329017],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07111429,0.0001702926,0.9263404,0.0006885988,0.0004736056,0.0007697646,0.00004241093,0.0003653906,0.00003526079],"genre_scores_gemma":[0.9895112,0.00004015942,0.007874417,0.00008193182,0.00004430084,0.001317518,0.000008354697,0.00005098885,0.001071162],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.918466,"threshold_uncertainty_score":0.9999418,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05413902585155395,"score_gpt":0.343047589407086,"score_spread":0.288908563555532,"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."}}