{"id":"W4230879051","doi":"10.32920/ryerson.14652336.v1","title":"Solving Channel Allocation by Reinforcement Learning in Cognitive Enabled Vehicular Ad Hoc Networks","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Reinforcement learning; Computer science; Vehicular ad hoc network; Dynamic programming; Channel (broadcasting); Wireless ad hoc network; Mathematical optimization; Channel allocation schemes; Q-learning; Cognitive radio; Artificial intelligence; Computer network; Wireless; Algorithm; Mathematics; Telecommunications","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":[0.0009845017,0.0005693224,0.0007624864,0.0003458933,0.0003470985,0.0006156605,0.0007497472,0.0007052742,0.0007563813],"category_scores_gemma":[0.002465153,0.0003664587,0.0003358347,0.0003005484,0.0008861995,0.0004516814,0.000740854,0.0007658654,0.00007908712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008789452,"about_ca_system_score_gemma":0.001494994,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01057224,"about_ca_topic_score_gemma":0.005248679,"domain_scores_codex":[0.9995748,0.0001846556,0.00001324721,0.00006479269,0.00007467059,0.00008780416],"domain_scores_gemma":[0.9989507,0.000743625,0.0001107676,0.00003154334,0.0001078734,0.00005543119],"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":[0.00001298582,0.00001633049,0.0001795644,0.00001185089,0.000009077794,0.00001561711,0.00001109321,0.9927337,0.0002105892,0.001795296,0.0001037795,0.004900074],"study_design_scores_gemma":[0.000004548165,0.000007252238,0.00002400527,0.000001019815,0.000001447322,0.000002311863,0.000002718497,0.9989405,0.000068611,0.0008999685,0.00004648996,0.000001107057],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05878603,0.0003016686,0.9374879,0.0002418554,0.00004087059,0.00005065554,0.00001682285,0.0001889376,0.002885224],"genre_scores_gemma":[0.9616851,0.0001481649,0.03651673,0.0000626369,0.00002162556,0.00008706645,0.0000178243,0.00001795615,0.001442827],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01057224,"threshold_uncertainty_score":0.02102143,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01001034878160181,"score_gpt":0.2218316033861517,"score_spread":0.2118212546045499,"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."}}