{"id":"W2847994468","doi":"10.1109/iccw.2018.8403619","title":"Energy-Efficient Power Allocation for Hybrid Multiple Access Systems","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Maximization; Mathematical optimization; Efficient energy use; Computer science; Transmitter power output; Energy (signal processing); Power (physics); Regular polygon; Spectral efficiency; Convex optimization; Mathematics; Telecommunications; Engineering; Electrical engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.0000541863,0.00009179082,0.00008916168,0.00008152954,0.00007309884,0.0000448829,0.0004396724,0.00004319433,0.00001439732],"category_scores_gemma":[0.00004822193,0.00008651629,0.000023688,0.0001159478,0.00005276776,0.0001176104,0.00008872944,0.00004065584,0.00001999926],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006339541,"about_ca_system_score_gemma":0.000005366668,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001778783,"about_ca_topic_score_gemma":0.00001549461,"domain_scores_codex":[0.9994921,0.000006141524,0.0001612498,0.0001144083,0.00006928541,0.00015683],"domain_scores_gemma":[0.9992307,0.0000865886,0.00002888673,0.0005137715,0.0001179692,0.00002204733],"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":[0.00001397777,0.00006326081,0.0001310183,0.00007023974,0.00005204067,3.096433e-7,0.0001058515,0.8674496,0.02191821,0.06461759,0.01211656,0.03346137],"study_design_scores_gemma":[0.0001433324,0.00001822202,0.00004442716,0.00001134191,0.000001519003,0.000001135866,0.00008567107,0.808797,0.1525639,0.0002093297,0.03801045,0.0001137293],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02280954,0.000324609,0.9715527,0.00006477139,0.0003491438,0.0001969519,0.000007365546,0.001673478,0.003021383],"genre_scores_gemma":[0.9940565,0.00004316743,0.005395263,0.0000231357,0.00003154965,0.0002363185,0.00001487237,0.00002687588,0.0001723392],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.971247,"threshold_uncertainty_score":0.3528032,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02131698469369717,"score_gpt":0.2626373932181834,"score_spread":0.2413204085244862,"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."}}