{"id":"W2099726047","doi":"10.1109/jsac.2011.110413","title":"QoS Provisioning for Heterogeneous Services in Cooperative Cognitive Radio Networks","year":2011,"lang":"en","type":"article","venue":"IEEE Journal on Selected Areas in Communications","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":108,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Quality of service; Cognitive radio; Computer network; Provisioning; Resource allocation; Blocking (statistics); Frequency allocation; Channel (broadcasting); Admission control; Channel allocation schemes; Resource management (computing); Call Admission Control; Distributed computing; Wireless; Wireless network; Telecommunications","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":[0.001741532,0.0006655469,0.0006180203,0.0004259282,0.0008563934,0.001458119,0.001547418,0.0008225964,0.0005185565],"category_scores_gemma":[0.002830781,0.0002333605,0.0004000117,0.0004837008,0.000761399,0.001344161,0.001447582,0.0007828331,0.0001531144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007519152,"about_ca_system_score_gemma":0.0009441665,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0024786,"about_ca_topic_score_gemma":0.002480715,"domain_scores_codex":[0.9989942,0.000319441,0.0000364641,0.0001258689,0.0003553412,0.0001687447],"domain_scores_gemma":[0.9989206,0.0005066916,0.0001270869,0.0001295424,0.0002130973,0.0001030692],"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":[0.0001724785,0.0001735901,0.001588273,0.0001852148,0.0001010897,0.0008910341,0.0006031402,0.7380599,0.02119861,0.1169879,0.002159673,0.117879],"study_design_scores_gemma":[0.000008726329,0.00002798466,0.0001304678,0.000005707469,0.00001638415,0.00007381906,0.00005775747,0.9826677,0.0008304331,0.01486713,0.001303207,0.00001066883],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06706898,0.001611625,0.9262819,0.0004409238,0.0001282103,0.00004560499,0.00001985245,0.000211086,0.004191823],"genre_scores_gemma":[0.9500317,0.0004686174,0.04821537,0.0001070041,0.0001174842,0.00005882981,0.00002042097,0.00002093916,0.0009596955],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0024786,"threshold_uncertainty_score":0.009210169,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03840817487842613,"score_gpt":0.2846773380749534,"score_spread":0.2462691631965273,"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."}}