{"id":"W2535730785","doi":"10.1109/camsap.2007.4498002","title":"Global D.C. Optimization for Multi-User Interference Systems","year":2007,"lang":"en","type":"article","venue":"","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Mathematical optimization; Optimization problem; Ergodic theory; Fading; Interference (communication); Convex optimization; Computer science; Global optimization; Gaussian; Function (biology); Channel (broadcasting); Linear programming; Convex function; Mathematics; Regular polygon; 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":[0.0001435742,0.0001164903,0.000118434,0.00004548744,0.00003129887,0.00003310649,0.00008828694,0.00008189899,0.00001635309],"category_scores_gemma":[0.00003251413,0.0001152357,0.00002693295,0.0001692906,0.000008840651,0.0002048671,0.0000111646,0.00002744504,0.00001157104],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001717379,"about_ca_system_score_gemma":0.000005447764,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001420831,"about_ca_topic_score_gemma":0.00005296543,"domain_scores_codex":[0.9993221,0.000005201497,0.0002705602,0.0001326779,0.00005255613,0.0002169055],"domain_scores_gemma":[0.9996423,0.00002751137,0.0000309376,0.000139248,0.0001027969,0.00005725502],"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":[0.000006356004,0.000007503013,0.0001485408,0.00006483305,0.00001051608,4.073544e-7,0.00002266709,0.9925583,0.0001153365,0.006033386,0.0003085267,0.0007236364],"study_design_scores_gemma":[0.000285096,0.00001556547,0.00003457759,0.00002947046,0.000005563271,0.000004943789,0.0001443284,0.9981723,0.0004459454,0.00001060774,0.0007116429,0.0001399116],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0002439451,0.0001645367,0.9943912,0.000003007728,0.000890127,0.000508226,0.00001344442,0.0006117065,0.003173802],"genre_scores_gemma":[0.5475191,0.000009404498,0.4515351,0.00001089549,0.00009654958,0.00004423706,0.00002909656,0.00003005472,0.0007255757],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5472752,"threshold_uncertainty_score":0.4699174,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02040909486772229,"score_gpt":0.2715434163041454,"score_spread":0.2511343214364231,"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."}}