{"id":"W2079281532","doi":"10.1002/ett.2736","title":"Joint subcarrier and power allocation in downlink OFDMA systems: an multi‐objective approach","year":2013,"lang":"en","type":"article","venue":"Transactions on Emerging Telecommunications Technologies","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Subcarrier; Sorting; Computer science; Orthogonal frequency-division multiple access; Telecommunications link; Mathematical optimization; Genetic algorithm; Resource allocation; Joint (building); Power (physics); Orthogonal frequency-division multiplexing; Algorithm; Engineering; Mathematics; Computer network","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"],"consensus_categories":[],"category_scores_codex":[0.0001302714,0.0002360996,0.0002364158,0.0006308146,0.0002519235,0.00006974375,0.0003767202,0.0002347324,0.00001723686],"category_scores_gemma":[0.00002385845,0.0002516508,0.0000341091,0.0008542224,0.0001515847,0.0006847177,0.00001694883,0.0006007755,0.00001167369],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001895782,"about_ca_system_score_gemma":0.00001314787,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000128149,"about_ca_topic_score_gemma":0.00007844024,"domain_scores_codex":[0.998861,0.00007272656,0.0003957985,0.0002818375,0.0001043806,0.0002842554],"domain_scores_gemma":[0.9987367,0.00006809901,0.00006710438,0.0009819479,0.000108723,0.00003745974],"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":[0.000002254926,0.0001463797,0.0001219984,0.00002452904,0.00003468138,1.200559e-7,0.0006093655,0.9593946,0.001218897,0.0005794058,0.00001697013,0.03785077],"study_design_scores_gemma":[0.0003254052,0.00003902351,0.002215101,0.00007039448,0.00001586699,0.000004744469,0.007361603,0.9875937,0.001657348,0.0002626697,0.0001305575,0.000323573],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08199845,0.001773209,0.91084,0.0005536128,0.000094773,0.0009771365,0.00001129737,0.00328136,0.000470122],"genre_scores_gemma":[0.8966705,0.003605422,0.09808197,0.000008117971,0.000003482928,0.001524473,0.00002678405,0.00004920999,0.00003005515],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8146721,"threshold_uncertainty_score":0.9999936,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0158966000707203,"score_gpt":0.2340538878985887,"score_spread":0.2181572878278684,"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."}}