{"id":"W777949165","doi":"10.1007/s10489-015-0682-x","title":"Optimizing channel selection for cognitive radio networks using a distributed Bayesian learning automata-based approach","year":2015,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Learning automata; Cognitive radio; Channel (broadcasting); Key (lock); Selection (genetic algorithm); Nash equilibrium; Network packet; Collision; Computer network; Point (geometry); Automaton; Distributed computing; Theoretical computer science; Machine learning; Mathematical optimization; Telecommunications; Wireless; Computer security","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":[0.0007758318,0.0003467101,0.0003797531,0.0001760299,0.0004888255,0.0003619743,0.0004344018,0.0001658253,0.00000246303],"category_scores_gemma":[0.0001295035,0.0003672513,0.0001228534,0.001165307,0.000100527,0.0003013521,0.0001225071,0.0004366704,0.000004777935],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002613286,"about_ca_system_score_gemma":0.0001922506,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003744951,"about_ca_topic_score_gemma":0.000005848743,"domain_scores_codex":[0.9975703,0.0001052417,0.0004047032,0.0008339105,0.000320273,0.0007655325],"domain_scores_gemma":[0.9985116,0.0004019317,0.0002451539,0.0002361306,0.0003350536,0.0002701315],"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":[0.00009858306,0.00008102171,0.00003641898,0.00001488053,0.00004265333,0.000003471224,0.0005680769,0.9538869,0.00008719856,0.006244274,0.00005668883,0.0388798],"study_design_scores_gemma":[0.0004781309,0.0001624265,0.00001299951,0.00007416733,0.00004141475,0.00004092056,0.0004532853,0.9933946,0.003508785,0.001260464,0.000118537,0.0004542905],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0008220723,0.0001564998,0.9964384,0.00005595549,0.0002447304,0.0007842105,0.000004278044,0.0005115447,0.000982289],"genre_scores_gemma":[0.815529,0.000008182014,0.1838829,0.0001278129,0.0002710101,0.00006654711,0.00006993103,0.00003532352,0.000009175717],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.814707,"threshold_uncertainty_score":0.9998779,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04115730657447952,"score_gpt":0.2681701940156795,"score_spread":0.2270128874412,"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."}}