{"id":"W2037307676","doi":"10.1109/tvt.2010.2044820","title":"A Dual-Decomposition-Based Resource Allocation for OFDMA Networks With Imperfect CSI","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"Blackberry (Canada); University of Waterloo","funders":"","keywords":"Computer science; Resource allocation; Quality of service; Channel state information; Orthogonal frequency-division multiple access; Frequency-division multiple access; Mathematical optimization; Computer network; Channel allocation schemes; Orthogonal frequency-division multiplexing; Channel (broadcasting); Wireless; Mathematics; Telecommunications","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":[0.00007698443,0.0002393267,0.000209948,0.0003581371,0.0002130381,0.00002190601,0.0001391615,0.0004506745,0.00002062171],"category_scores_gemma":[0.000003671699,0.0002403764,0.00007697361,0.0006505785,0.0001115927,0.00009681395,6.599913e-7,0.0006256507,0.000005875463],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006836263,"about_ca_system_score_gemma":0.00002112426,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002137316,"about_ca_topic_score_gemma":0.00008225281,"domain_scores_codex":[0.9990162,0.0000153412,0.0002197402,0.0003046136,0.0001111894,0.0003329422],"domain_scores_gemma":[0.9992572,0.0001048266,0.0000522193,0.0004204925,0.0001089117,0.00005633593],"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":[0.00005867859,0.0000635072,0.00001103997,0.00002075177,0.00005712551,0.000003112672,0.000009773421,0.9488767,0.02910789,0.0001515581,0.00005870234,0.02158117],"study_design_scores_gemma":[0.0008083271,0.0001750741,0.00001080369,0.00004392796,0.00006208837,0.00002090623,0.00001284492,0.8670355,0.1300119,0.00005283453,0.001515637,0.0002501632],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09292068,0.00005528714,0.9040921,0.0005485342,0.0002870085,0.0005893761,0.00001500126,0.001461521,0.00003054162],"genre_scores_gemma":[0.962815,0.00003318427,0.03596224,0.00007324089,0.00006835062,0.0008899548,0.00003929402,0.00009701087,0.00002171669],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8698943,"threshold_uncertainty_score":0.9802265,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003081321085178687,"score_gpt":0.2052876131333721,"score_spread":0.2022062920481934,"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."}}