{"id":"W2141004812","doi":"10.1109/cnsr.2011.10","title":"Collaborative Spectrum Sensing in Cognitive Radio System - Performance Analysis of Weighted Gain Combining","year":2011,"lang":"en","type":"article","venue":"","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Northern British Columbia; University of British Columbia","funders":"","keywords":"Cognitive radio; Interference (communication); Computer science; Channel (broadcasting); Probability density function; Energy (signal processing); Radio spectrum; Outage probability; Scheme (mathematics); Spectrum (functional analysis); Electronic engineering; Telecommunications; Wireless; Mathematics; Statistics; Engineering; Fading; Physics","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":[0.0005586645,0.0002228791,0.0006544833,0.0009492291,0.0001190519,0.00006065343,0.0002578596,0.00007479764,0.00002578263],"category_scores_gemma":[0.00002197077,0.0002082059,0.0001225877,0.005697578,0.0001012811,0.0004197248,0.0001168751,0.0001909738,0.000005893502],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001386858,"about_ca_system_score_gemma":0.00009734339,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000298449,"about_ca_topic_score_gemma":0.0005124714,"domain_scores_codex":[0.9980266,0.000227196,0.0005291699,0.0005038474,0.0002802652,0.0004329002],"domain_scores_gemma":[0.9988193,0.0002909953,0.0002604674,0.0003080208,0.000229915,0.00009128368],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001097111,0.00118097,0.2921959,0.0003297604,0.01009334,0.002134502,0.1265948,0.003822327,0.00239204,0.3707531,0.0001425358,0.1892636],"study_design_scores_gemma":[0.0006243138,0.0001398025,0.04898856,0.0002555616,0.0001991098,0.0000202896,0.002212262,0.9365051,0.01065554,0.0001229437,0.000002398795,0.0002741409],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6471037,0.00009433321,0.3174587,0.00002854935,0.0001358212,0.000209201,0.000003863274,0.0001139961,0.0348518],"genre_scores_gemma":[0.986941,0.00002735233,0.01290636,0.00004419954,0.00002407621,0.000001603249,0.000005787784,0.0000109455,0.00003864994],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9326828,"threshold_uncertainty_score":0.8490392,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01658726298743996,"score_gpt":0.2276477769713429,"score_spread":0.2110605139839029,"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."}}