{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004330826,0.0002240164,0.0002947665,0.0003002509,0.001210902,0.0003786003,0.004084349,0.0002628524,0.000005179911],"category_scores_gemma":[0.0008737074,0.0002460702,0.0001011117,0.0002896983,0.00009516106,0.0006405297,0.0002560361,0.001518497,0.000008427721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007988292,"about_ca_system_score_gemma":0.00006816205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000933028,"about_ca_topic_score_gemma":0.0005827963,"domain_scores_codex":[0.9985715,0.00009751847,0.0005629379,0.0001564651,0.0001995886,0.0004119948],"domain_scores_gemma":[0.9950978,0.001248428,0.0005888779,0.002554193,0.0004321317,0.00007854287],"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":[0.00006668756,0.0004488996,0.06201228,0.00003806873,0.0001669425,0.000003152295,0.0001823216,0.8979468,0.005964181,0.0008830052,0.002254011,0.03003366],"study_design_scores_gemma":[0.001261496,0.00004942788,0.1125723,0.0001647796,0.00001882192,0.000009692794,0.00004104228,0.8780277,0.002093772,0.001609241,0.003798632,0.0003531413],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4785184,0.002370117,0.4969861,0.005674654,0.001997287,0.001989608,0.0001561519,0.001669334,0.01063831],"genre_scores_gemma":[0.9792434,0.00714344,0.01307152,0.00004260464,0.00004844669,0.0002226443,0.00005523705,0.00006264422,0.0001100668],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.500725,"threshold_uncertainty_score":0.9999992,"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."}}