{"id":"W2095786201","doi":"10.1109/icccn.2011.6006078","title":"Analysis of Cognitive Radio Networks with Channel Aggregation and Imperfect Sensing","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 Manitoba","funders":"","keywords":"Cognitive radio; Bandwidth (computing); Markov chain; Computer science; Throughput; Imperfect; Channel (broadcasting); Computer network; Markov process; Algorithm; Telecommunications; Mathematics; Wireless; Statistics; Machine learning","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":[0.002774958,0.00111906,0.001137967,0.001061979,0.0007721503,0.001732992,0.001659296,0.0009692389,0.001465879],"category_scores_gemma":[0.01053609,0.0007647204,0.0006705234,0.0008966022,0.00195942,0.00204456,0.00130215,0.0008825765,0.0001709632],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002729622,"about_ca_system_score_gemma":0.001523019,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01466057,"about_ca_topic_score_gemma":0.006339519,"domain_scores_codex":[0.9984406,0.0004748506,0.00003947596,0.0001530764,0.0004794571,0.000412628],"domain_scores_gemma":[0.9933169,0.004794862,0.0008027758,0.0002790958,0.0006167782,0.0001896287],"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":[0.00002955347,0.00001242551,0.0004709431,0.00002838378,0.0000245815,0.00008235552,0.00004808884,0.9742911,0.0005405593,0.02221763,0.0002850818,0.001969251],"study_design_scores_gemma":[0.000003252213,0.000008210068,0.0002218447,0.000004310414,0.000007854262,0.0000196334,0.00001090416,0.9929435,0.0001029571,0.006562566,0.000109975,0.000005080222],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2660899,0.002717572,0.7055827,0.001397112,0.0001504366,0.0001184094,0.0003028359,0.000353043,0.02328813],"genre_scores_gemma":[0.9901751,0.0006683441,0.006410447,0.00007767163,0.00006981702,0.00005898896,0.00006062326,0.00003222989,0.002446762],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01466057,"threshold_uncertainty_score":0.02915049,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01471852665177651,"score_gpt":0.2127016333416274,"score_spread":0.1979831066898509,"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."}}