{"id":"W2140264163","doi":"10.1109/vtcf.2006.266","title":"Branch-and-Bound Approach to OFDMA Radio Resource Allocation","year":2006,"lang":"en","type":"article","venue":"IEEE Vehicular Technology Conference","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Orthogonal frequency-division multiple access; Computer science; Computational complexity theory; Orthogonal frequency-division multiplexing; Resource allocation; Frequency-division multiple access; Mathematical optimization; Upper and lower bounds; Algorithm; Channel (broadcasting); Channel allocation schemes; Wireless; Mathematics; Computer network; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002607409,0.001356943,0.001939529,0.0009664103,0.000936551,0.002361601,0.001227269,0.001324219,0.003467178],"category_scores_gemma":[0.007087759,0.0006166788,0.0004098409,0.002084334,0.001291537,0.001634373,0.001106132,0.001997517,0.001214651],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00123291,"about_ca_system_score_gemma":0.002584615,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003217871,"about_ca_topic_score_gemma":0.003143105,"domain_scores_codex":[0.9975937,0.001370891,0.00006441083,0.000159535,0.0006495444,0.0001617948],"domain_scores_gemma":[0.9971911,0.002151069,0.0001185293,0.0001258933,0.0003607667,0.00005260463],"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":[0.00007828418,0.00008030803,0.0002396044,0.0001096224,0.00005415155,0.00004529989,0.00005514839,0.7925715,0.0006852847,0.1022515,0.002957946,0.1008713],"study_design_scores_gemma":[0.000008804131,0.00001905325,0.00002651722,0.000007878256,0.000005434931,0.00001631953,0.00000663751,0.9778489,0.0001808536,0.02091417,0.0009611965,0.000004257513],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00182113,0.001085395,0.993188,0.0001802155,0.0000551038,0.0000260713,0.00001638703,0.00007993795,0.00354778],"genre_scores_gemma":[0.3476233,0.004125826,0.6405042,0.0003954519,0.0003748832,0.0003980984,0.0001366884,0.0001377652,0.006303699],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003467178,"threshold_uncertainty_score":0.01378942,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006710450578364243,"score_gpt":0.1890286366930098,"score_spread":0.1823181861146456,"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."}}