{"id":"W2490558322","doi":"10.1002/wcm.2713","title":"Adaptive channel selection and slot length configuration in cognitive radio","year":2016,"lang":"en","type":"article","venue":"Wireless Communications and Mobile Computing","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Channel (broadcasting); Cognitive radio; Idle; Selection (genetic algorithm); Markov process; Process (computing); Markov chain; Channel state information; Algorithm; Computer network; Real-time computing; Telecommunications; Artificial intelligence; Machine learning; Mathematics; Wireless; Statistics","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":[0.001348344,0.0005196948,0.000614183,0.000517161,0.0004248747,0.000724631,0.001050858,0.0004536763,0.0005214611],"category_scores_gemma":[0.003836201,0.0003255268,0.0002649303,0.0005629395,0.001118824,0.0008314327,0.0005744395,0.0005371519,0.00009789493],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009218754,"about_ca_system_score_gemma":0.0007875991,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002446624,"about_ca_topic_score_gemma":0.001780435,"domain_scores_codex":[0.998834,0.0004915307,0.00003306368,0.0002104729,0.0002222248,0.0002087143],"domain_scores_gemma":[0.9978625,0.001361987,0.0003149208,0.000129003,0.0002074077,0.0001242172],"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":[0.0003274728,0.0001052398,0.001660861,0.00004972306,0.00004290658,0.0001519454,0.0000921574,0.9295129,0.007534933,0.01032669,0.0004403586,0.04975478],"study_design_scores_gemma":[0.00001586674,0.00006318625,0.000317032,0.000004253225,0.00001113242,0.000039586,0.00001114596,0.9953467,0.001265538,0.002732097,0.0001824537,0.00001098627],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1111602,0.0007123447,0.885427,0.0001126351,0.00007109395,0.00004732772,0.00002028747,0.0002568303,0.002192275],"genre_scores_gemma":[0.9811288,0.00008711156,0.01838414,0.00002001956,0.00002344097,0.00001880638,0.000007379984,0.00000908041,0.000321313],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002446624,"threshold_uncertainty_score":0.007130802,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0257453399077453,"score_gpt":0.2653454458859417,"score_spread":0.2396001059781964,"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."}}