{"id":"W2048047401","doi":"10.1109/mwc.2007.314548","title":"Radio resource management games in wireless networks: an approach to bandwidth allocation and admission control for polling service in IEEE 802.16 [Radio Resource Management and Protocol Engineering for IEEE 802.16]","year":2007,"lang":"en","type":"article","venue":"IEEE Wireless Communications","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":77,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba; Tellabs (Canada)","funders":"University of Manitoba","keywords":"Computer science; Game theory; Polling; Computer network; Wireless network; Resource allocation; Resource management (computing); Quality of service; Nash equilibrium; Admission control; Call Admission Control; Radio resource management; Bandwidth allocation; Wireless; Telecommunications; Mathematical optimization","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001182547,0.0005050814,0.0005644692,0.0006545138,0.00031168,0.0001349119,0.0008172807,0.0002474868,6.912573e-7],"category_scores_gemma":[0.00001358107,0.0006000242,0.00006183072,0.001048547,0.00006011211,0.0004182844,0.0001135798,0.0004050721,5.32489e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006023222,"about_ca_system_score_gemma":0.00001613905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003359156,"about_ca_topic_score_gemma":0.0003568053,"domain_scores_codex":[0.9971932,0.0001285001,0.0009590166,0.0006680475,0.0002481684,0.0008030306],"domain_scores_gemma":[0.9975634,0.0004824873,0.0001834239,0.001369475,0.0001036146,0.0002975697],"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":[0.0002988141,0.0001996379,0.000283301,0.0008686404,0.00007845536,0.000001352824,0.0008049628,0.959451,0.0004754356,0.002528894,0.0001891681,0.03482029],"study_design_scores_gemma":[0.004073899,0.00006849966,0.001202706,0.0007924735,0.0000625724,0.000006042638,0.0008889139,0.9834102,0.0004346328,0.00007267236,0.008352769,0.0006346208],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01866791,0.00034348,0.941864,0.0002059421,0.0000928831,0.03800874,0.00001909386,0.0003409816,0.0004569948],"genre_scores_gemma":[0.8548087,0.0003495022,0.08911296,0.000178968,0.0001596203,0.05492736,0.0001847787,0.0002033729,0.00007471628],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.852751,"threshold_uncertainty_score":0.9996451,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01649155635268073,"score_gpt":0.2591138468979404,"score_spread":0.2426222905452596,"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."}}