{"id":"W2553813946","doi":"10.1109/jsen.2016.2627884","title":"Joint Optimal Transmission Power and Sensing Time for Energy Efficient Spectrum Sensing in Cognitive Radio System","year":2016,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Cognitive radio; Transmission (telecommunications); Joint (building); Computer science; Energy (signal processing); Computational complexity theory; Power (physics); Efficient energy use; Iterative method; Electronic engineering; Mathematical optimization; Algorithm; Real-time computing; Telecommunications; Wireless; Engineering; Electrical engineering; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.0008926652,0.0003307478,0.0005003974,0.0004294896,0.0004067537,0.0003263795,0.0001309139,0.0001273336,0.000007202351],"category_scores_gemma":[0.00005227541,0.0002421826,0.0002011934,0.0003038565,0.0001067267,0.0002609134,0.00004144249,0.0002713636,0.000005930609],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003101305,"about_ca_system_score_gemma":0.0001043758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001315914,"about_ca_topic_score_gemma":0.000004473563,"domain_scores_codex":[0.997339,0.0002955163,0.0005957291,0.0005884976,0.0003810778,0.0008001914],"domain_scores_gemma":[0.9984773,0.0005688743,0.0002562158,0.0001813982,0.0001853188,0.0003308316],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008422525,0.0001699309,0.00004294625,0.00006492753,0.0002952432,0.005177583,0.004803154,0.01594405,0.2819644,0.002107407,0.0009013084,0.6876868],"study_design_scores_gemma":[0.002806148,0.0002779936,0.0002378051,0.002075727,0.00003594158,0.014091,0.0002166182,0.9312044,0.04797859,0.0002872956,0.000276863,0.0005116104],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3969744,0.0002004987,0.6011415,0.0007819894,0.0004524357,0.000133856,0.000002929181,0.00005968063,0.0002527109],"genre_scores_gemma":[0.9812643,0.00006679868,0.01796878,0.0000839073,0.0004515157,3.236044e-7,4.219351e-7,0.00003739567,0.0001265801],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9152604,"threshold_uncertainty_score":0.9875921,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01118728553598685,"score_gpt":0.2199879906405059,"score_spread":0.208800705104519,"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."}}