{"id":"W2109495118","doi":"10.1109/tvt.2006.877470","title":"Computationally Efficient Method to Evaluate the Performance of Guard-Channel-Based Call Admission Control in Cellular Networks","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Wireless Communication Networks Research","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Handover; Call Admission Control; Computer science; Call blocking; Markov chain; Blocking (statistics); Bounding overwatch; Guard (computer science); Channel (broadcasting); Computational complexity theory; Quality of service; Cellular network; Markov process; Computer network; Mathematical optimization; Algorithm; Mathematics; Wireless; Wireless network; Telecommunications; Artificial intelligence","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":[0.00146646,0.000867406,0.0007768787,0.0009512397,0.000474849,0.0007036931,0.0008857863,0.0007511071,0.002155358],"category_scores_gemma":[0.009269502,0.0002334991,0.0003141261,0.0007589972,0.0005836966,0.0008947703,0.0005764192,0.0007825941,0.0003076633],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001207192,"about_ca_system_score_gemma":0.001987452,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008825884,"about_ca_topic_score_gemma":0.005946044,"domain_scores_codex":[0.9990663,0.0003351863,0.00004628399,0.00006125046,0.0004117795,0.0000792506],"domain_scores_gemma":[0.9965035,0.002507817,0.0002138387,0.0002468089,0.0004748165,0.00005321957],"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":[0.0000845363,0.00006437115,0.0007461451,0.00004539451,0.00002382208,0.00002997694,0.00002065496,0.959597,0.002192181,0.007710533,0.0003908078,0.02909458],"study_design_scores_gemma":[0.000004411913,0.00001122586,0.00007509518,0.000001663741,0.00000202299,0.000006449407,0.000002844175,0.9988787,0.0004766321,0.000462255,0.00007631603,0.000002495673],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04611538,0.0002546682,0.9494313,0.00008311943,0.0000509403,0.0001143051,0.00007567929,0.0007575555,0.003116964],"genre_scores_gemma":[0.7382608,0.0002412765,0.2594523,0.00006172824,0.00003536445,0.0003773994,0.0001795734,0.00007963158,0.001311841],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008825884,"threshold_uncertainty_score":0.01754904,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01145847364375066,"score_gpt":0.2733072258939327,"score_spread":0.2618487522501821,"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."}}