{"id":"W2020130667","doi":"10.1109/iscc.2013.6755024","title":"Exploiting multiuser diversity for OFDMA next generation wireless networks","year":2013,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Computer network; Orthogonal frequency-division multiple access; Quality of service; WiMAX; Scheduling (production processes); Interoperability; Frequency-division multiple access; Wireless network; Exploit; Orthogonal frequency-division multiplexing; Throughput; Cellular network; Next-generation network; Wireless; Telecommunications; Engineering; The Internet","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":[0.00004339431,0.0001309365,0.0001191138,0.00003606844,0.0002092894,0.00005995893,0.00009390351,0.00008890899,0.0001413288],"category_scores_gemma":[0.000007361511,0.0001369412,0.0000411775,0.0001113463,0.00001185423,0.0007445335,0.00006705911,0.0000741945,0.00001817478],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005636488,"about_ca_system_score_gemma":0.000002300967,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002849189,"about_ca_topic_score_gemma":0.00002855036,"domain_scores_codex":[0.9993431,0.000009182212,0.0001587227,0.0001555728,0.00007637755,0.0002570631],"domain_scores_gemma":[0.9996381,0.00005309759,0.00003018744,0.0001236244,0.00009518628,0.00005979853],"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":[0.00000135349,0.000006024989,0.0006622007,0.00001169767,0.00001423958,1.286262e-7,0.00007382665,0.9558784,0.003366366,0.0002041868,0.003493272,0.03628824],"study_design_scores_gemma":[0.0002674275,0.000006920547,0.0002445791,0.00001084482,0.000008283256,2.772905e-7,0.00006907496,0.9971113,0.001928539,0.00004143152,0.0001346929,0.0001766191],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2405485,0.00008374537,0.7580332,0.00002770009,0.0003173855,0.0003704391,0.00000129323,0.0004007755,0.0002169633],"genre_scores_gemma":[0.9612485,0.0002020129,0.03748107,0.00008456683,0.000509617,0.0001776284,0.00005521708,0.0000428008,0.0001985804],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7207,"threshold_uncertainty_score":0.5584299,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03838126574831443,"score_gpt":0.2064653111252781,"score_spread":0.1680840453769637,"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."}}