{"id":"W4233552021","doi":"10.32920/ryerson.14653566","title":"Efficient techniques for cooperative spectrum sensing in cognitive radio networks","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Distributed Sensor Networks and Detection Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Cognitive radio; Computer science; False alarm; Overhead (engineering); Reliability (semiconductor); Fusion center; Detector; Dual (grammatical number); Channel (broadcasting); Throughput; Mathematical optimization; Algorithm; Artificial intelligence; Wireless; Computer network; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004704051,0.0003938958,0.0005680434,0.0002131476,0.000148394,0.0008121256,0.000427171,0.0004153909,0.0000230375],"category_scores_gemma":[0.00008618275,0.0003860589,0.0002231424,0.0006564605,0.00006636749,0.00006959492,0.0008979201,0.0008503417,0.000002049554],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002229959,"about_ca_system_score_gemma":0.0002116273,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001104317,"about_ca_topic_score_gemma":0.0001631448,"domain_scores_codex":[0.9974203,0.0001621504,0.0005015188,0.001123876,0.0002424861,0.0005496558],"domain_scores_gemma":[0.9984335,0.0003487882,0.0001887526,0.0005190392,0.0003889421,0.0001209802],"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":[0.00005396371,0.0002596864,0.00001269366,0.00004956112,0.0001475539,0.0002423041,0.0006695258,0.850893,0.00002747692,0.003686347,0.001660387,0.1422975],"study_design_scores_gemma":[0.0003350957,0.00008129853,0.00004918749,0.0003756254,0.00001517059,0.00003621493,0.0002054888,0.9943911,0.003338227,0.0004058883,0.0002928255,0.0004738818],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001949943,0.0003594625,0.991907,0.0004116163,0.00147339,0.001228349,0.00002459204,0.0004972274,0.002148407],"genre_scores_gemma":[0.8313274,0.00009497909,0.1668471,0.0005388883,0.0005117815,0.0001041051,0.0001666221,0.00003703651,0.0003721266],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8293774,"threshold_uncertainty_score":0.9998592,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01823372715012339,"score_gpt":0.2669804606086368,"score_spread":0.2487467334585134,"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."}}