{"id":"W2736111490","doi":"10.1109/jsac.2017.2725178","title":"Dynamic Cell Association for Non-Orthogonal Multiple-Access V2S Networks","year":2017,"lang":"en","type":"article","venue":"IEEE Journal on Selected Areas in Communications","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":72,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Science Foundation of Zhejiang Province; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Karush–Kuhn–Tucker conditions; Computer science; Optimization problem; Power control; Transmitter power output; Utility maximization problem; Mathematical optimization; Scheduling (production processes); Base station; Handover; Spectral efficiency; Maximization; Small cell; Computer network; Power (physics); Utility maximization; Algorithm; Transmitter","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005958689,0.0006908544,0.0005676018,0.0003276143,0.0007791066,0.000972669,0.0009032703,0.0005918158,0.002054733],"category_scores_gemma":[0.001810621,0.000211986,0.0003871739,0.0008785454,0.000614654,0.0007456004,0.001094949,0.0009987525,0.0005399488],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006951019,"about_ca_system_score_gemma":0.001382386,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003520699,"about_ca_topic_score_gemma":0.006174403,"domain_scores_codex":[0.9992988,0.000227891,0.00002090573,0.0001080708,0.0002419753,0.0001024467],"domain_scores_gemma":[0.9995372,0.0002175952,0.00006200562,0.00005400414,0.0001038067,0.00002541442],"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":[0.00009487534,0.00007524558,0.0006782056,0.0001431195,0.00004163115,0.0002902307,0.0001212146,0.7843048,0.007013917,0.08321029,0.004799894,0.1192266],"study_design_scores_gemma":[0.000006706889,0.00003814103,0.0001023902,0.000005369627,0.000005659774,0.00007333417,0.00001836907,0.987564,0.0005979766,0.009379181,0.002201328,0.000007407322],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01018751,0.0009463186,0.9835931,0.0001991849,0.0001027056,0.00005318407,0.00003813736,0.0001202093,0.004759684],"genre_scores_gemma":[0.8638301,0.00208629,0.1273025,0.000209497,0.0001986018,0.0003051017,0.0001887941,0.00005200485,0.005827164],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003520699,"threshold_uncertainty_score":0.007000387,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02656748156470124,"score_gpt":0.3139553542890991,"score_spread":0.2873878727243979,"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."}}