{"id":"W2032906529","doi":"10.1109/vetecf.2007.374","title":"A Novel Subcarrier Allocation Algorithm for Multiuser OFDM System With Fairness: User's Perspective","year":2007,"lang":"en","type":"article","venue":"IEEE Vehicular Technology Conference","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Subcarrier; Computer science; Telecommunications link; Orthogonal frequency-division multiplexing; Base station; Bit error rate; Max-min fairness; Resource allocation; Channel (broadcasting); Transmitter power output; Transmission (telecommunications); Multiuser detection; Algorithm; Fairness measure; Computer network; Mathematical optimization; Real-time computing; Wireless; Transmitter; Telecommunications; Code division multiple access; Mathematics; Throughput","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":[0.0001739409,0.0002892869,0.0003003651,0.0003336739,0.00011291,0.00002784543,0.00027776,0.0004294133,0.000002384919],"category_scores_gemma":[0.00002472498,0.0002817512,0.00004491275,0.000681288,0.0001676212,0.0002148153,0.00002099041,0.0002897412,0.000007880695],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003760611,"about_ca_system_score_gemma":0.00005017414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001968322,"about_ca_topic_score_gemma":0.0001351515,"domain_scores_codex":[0.9986504,0.000008872565,0.0002795557,0.0004212869,0.0001705421,0.0004693574],"domain_scores_gemma":[0.9986421,0.00005459148,0.00009668343,0.0004197326,0.0007207934,0.00006607834],"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":[0.00006993223,0.0001003951,0.0005021847,0.0001748903,0.0003687605,0.00005206074,0.0005148067,0.7858648,0.06312288,0.1007713,0.00006478841,0.04839313],"study_design_scores_gemma":[0.001138397,0.0001003256,0.0001276049,0.0001613036,0.00004892262,0.00007058695,0.002103849,0.933235,0.06190548,0.000188889,0.0004934052,0.0004262298],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02903923,0.0001742484,0.9677526,0.00008352084,0.0002617938,0.0007989852,0.00001847683,0.00169909,0.0001720523],"genre_scores_gemma":[0.803012,0.00001800937,0.1965465,0.00001180086,0.00006723239,0.0002162723,0.0000183805,0.0000678471,0.00004194048],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7739728,"threshold_uncertainty_score":0.9999635,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008065143597554437,"score_gpt":0.2213579867546369,"score_spread":0.2132928431570824,"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."}}