{"id":"W2035307429","doi":"10.1109/twc.2014.012814.131045","title":"Routing, Scheduling and Power Allocation in Generic OFDMA Wireless Networks: Optimal Design and Efficiently Computable Bounds","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Scheduling (production processes); Mathematical optimization; Geometric programming; Upper and lower bounds; Monomial; Wireless network; Job shop scheduling; Linear programming; Wireless; Integer programming; Routing (electronic design automation); Mathematics; Algorithm; Computer network; Discrete mathematics","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":[0.002323764,0.001531826,0.001355942,0.0007182731,0.0005102044,0.002093114,0.001121062,0.001624109,0.001658772],"category_scores_gemma":[0.007874762,0.0007280639,0.0007837307,0.001505225,0.001588342,0.002116368,0.001294406,0.001490136,0.0002462033],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002200508,"about_ca_system_score_gemma":0.001565015,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001899797,"about_ca_topic_score_gemma":0.002092608,"domain_scores_codex":[0.9989108,0.0004863069,0.00003805071,0.0001504501,0.0002160181,0.0001983364],"domain_scores_gemma":[0.9981915,0.00131271,0.0002277212,0.0001004473,0.0001008571,0.00006659516],"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":[0.00002269368,0.00002456465,0.0001576674,0.00006480414,0.00001179714,0.000033732,0.0000205622,0.9594218,0.000611876,0.03184797,0.0004103344,0.007372385],"study_design_scores_gemma":[0.000009589304,0.00001909826,0.00006026351,0.0000105388,0.000006283112,0.00001573482,0.00001489371,0.9773427,0.0003279044,0.02173328,0.0004553903,0.000004429904],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04573424,0.0008755366,0.9431059,0.0005522083,0.000046394,0.00009477056,0.0001689542,0.0001114991,0.009310534],"genre_scores_gemma":[0.7124893,0.001963716,0.2815061,0.0002143783,0.0001030968,0.0003239764,0.0003019049,0.0001295038,0.002968155],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002323764,"threshold_uncertainty_score":0.01596594,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01474317280129721,"score_gpt":0.2296899002534231,"score_spread":0.2149467274521259,"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."}}