{"id":"W2141290401","doi":"10.1109/iciinfs.2011.6038032","title":"Spectrum sensing in low SNR: Diversity combining and cooperative communications","year":2011,"lang":"en","type":"article","venue":"","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":"Cognitive radio; False alarm; Detector; Computer science; Fading; Monte Carlo method; Signal-to-noise ratio (imaging); Cooperative diversity; Energy (signal processing); Maximal-ratio combining; Bit error rate; Diversity combining; Detection theory; Electronic engineering; Statistical power; Algorithm; Constant false alarm rate; Telecommunications; Statistics; Wireless; Mathematics; Artificial intelligence; Engineering; Decoding methods","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":[0.001631568,0.0007494504,0.0008007307,0.0004157121,0.0003911138,0.001287159,0.0006711233,0.001193416,0.0004593596],"category_scores_gemma":[0.00896969,0.0002974549,0.0003150171,0.0003641345,0.00166092,0.001554408,0.001241902,0.0005822889,0.0001811129],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004714046,"about_ca_system_score_gemma":0.0003068241,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003620671,"about_ca_topic_score_gemma":0.0003989403,"domain_scores_codex":[0.9984015,0.0005747134,0.00004578352,0.0001966795,0.0006142557,0.0001671202],"domain_scores_gemma":[0.9953291,0.003756451,0.0003578723,0.0001888093,0.0003029862,0.00006469284],"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":[0.0006835663,0.0001820483,0.003958539,0.000455801,0.000175163,0.001472234,0.0006630687,0.7611272,0.09059101,0.06660912,0.0005043071,0.07357786],"study_design_scores_gemma":[0.00003337382,0.0004222996,0.001161631,0.00003558497,0.00004833071,0.001012644,0.0001193894,0.9451557,0.01698076,0.03435934,0.0006256264,0.00004534862],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2440797,0.001810509,0.7460974,0.0003574472,0.00004814741,0.00003526298,0.00001914825,0.0001536991,0.007398599],"genre_scores_gemma":[0.9831146,0.0002974737,0.01610854,0.00007965771,0.00002928661,0.000016031,0.000006627721,0.000008889829,0.0003387605],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001631568,"threshold_uncertainty_score":0.008628666,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03965273950394579,"score_gpt":0.2338816444306602,"score_spread":0.1942289049267144,"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."}}