{"id":"W4312443608","doi":"10.1109/ithings-greencom-cpscom-smartdata-cybermatics55523.2022.00057","title":"Demand-Oriented Allocation with Fairness in Multi-Operator Dynamic Spectrum Sharing Systems","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics)","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Stackelberg competition; Frequency allocation; Computer science; Operator (biology); Mathematical optimization; Nash equilibrium; Convex optimization; Game theory; Spectrum (functional analysis); Optimization problem; Incentive; Regular polygon; Mathematical economics; Mathematics; Algorithm; Computer network; Microeconomics; Economics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00235188,0.001078996,0.001595766,0.0008148588,0.002085707,0.001558287,0.004118791,0.0002619562,0.00001012839],"category_scores_gemma":[0.0001847314,0.001066089,0.000177031,0.0009232908,0.001032988,0.001709003,0.002542601,0.002317323,0.00001205832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002853162,"about_ca_system_score_gemma":0.0002384864,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003512494,"about_ca_topic_score_gemma":0.003031071,"domain_scores_codex":[0.9926375,0.001108603,0.002058272,0.001773794,0.001465166,0.0009566704],"domain_scores_gemma":[0.9929658,0.002084988,0.001766192,0.002177808,0.0005627849,0.0004423831],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003949976,0.0207907,0.0708162,0.007490867,0.01518139,0.000302242,0.3124309,0.05277589,0.01823837,0.1808855,0.05830136,0.2588367],"study_design_scores_gemma":[0.002432175,0.0003369534,0.002106586,0.001755747,0.0001791932,0.0001615344,0.0009744255,0.9847923,0.00009663601,0.0008646922,0.005017234,0.001282548],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8368791,0.0003714327,0.1548149,0.001738523,0.004084665,0.00103993,0.0004798398,0.0002237628,0.0003678932],"genre_scores_gemma":[0.9877041,0.0004590012,0.009126763,0.0008741554,0.0003981025,0.00003299411,0.0009581365,0.00008784874,0.0003589073],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9320164,"threshold_uncertainty_score":0.9999844,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06798638962346536,"score_gpt":0.3232674886642113,"score_spread":0.255281099040746,"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."}}