{"id":"W2142181899","doi":"10.1109/twc.2011.072011.110406","title":"Capacity Analysis and Call Admission Control in Distributed Cognitive Radio Networks","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Aloha; Computer science; Cognitive radio; Computer network; Network packet; Channel (broadcasting); Call Admission Control; Access control; Control channel; Throughput; Random access; Markov chain; Wireless network; Base station; Telecommunications; Wireless","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":[0.002779324,0.001010402,0.0008282097,0.0016317,0.000716016,0.0018784,0.001893793,0.0009706412,0.001747027],"category_scores_gemma":[0.01396799,0.0004187346,0.0005661208,0.001297668,0.002360922,0.002572452,0.001209135,0.001132606,0.0002079547],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003361572,"about_ca_system_score_gemma":0.002313306,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007931148,"about_ca_topic_score_gemma":0.00268044,"domain_scores_codex":[0.9972596,0.0009660844,0.00007697101,0.0002601024,0.0009370525,0.0005001888],"domain_scores_gemma":[0.9893723,0.008081565,0.0005844114,0.0005460399,0.001203829,0.0002118741],"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":[0.00004817737,0.00003042142,0.0003326985,0.00005502963,0.00002606993,0.00006210948,0.00007949608,0.9176475,0.001344063,0.07137581,0.0004483701,0.008550213],"study_design_scores_gemma":[0.000002234983,0.000004842342,0.00005693945,0.000004911744,0.00000336794,0.000008977585,0.000008222832,0.9862664,0.0002864201,0.0132116,0.0001408047,0.000005236135],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05046977,0.001515982,0.9375711,0.0004601793,0.00006854615,0.00005116035,0.00009408599,0.0003941614,0.009375066],"genre_scores_gemma":[0.9769513,0.0006218062,0.02074848,0.00008326156,0.0001053376,0.000108051,0.00005987928,0.0000615034,0.00126043],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007931148,"threshold_uncertainty_score":0.02438998,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02913284002835315,"score_gpt":0.2469385180079572,"score_spread":0.2178056779796041,"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."}}