{"id":"W2325466454","doi":"10.1109/tcomm.2016.2520458","title":"Optimal Power Allocation for Maximum Throughput of General MU-MIMO Multiple Access Channels With Mixed Constraints","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Throughput; MIMO; Computer science; Power (physics); Electronic engineering; Computer network; Max-min fairness; Channel (broadcasting); Mathematical optimization; Engineering; Resource allocation; Telecommunications; Mathematics; Wireless; Physics","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":[0.0001352524,0.000204483,0.0002330523,0.000168631,0.0002081728,0.00002874862,0.0005873596,0.0001095728,0.00004385348],"category_scores_gemma":[0.00001536277,0.0001710089,0.00008731389,0.000271562,0.0002501012,0.0005315864,0.000004958958,0.0001230785,0.0000116882],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001226161,"about_ca_system_score_gemma":0.00004642175,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002013073,"about_ca_topic_score_gemma":0.0001715747,"domain_scores_codex":[0.998977,0.00006435825,0.0004028463,0.0002033317,0.0001198147,0.0002326355],"domain_scores_gemma":[0.9979504,0.0004295545,0.0001129727,0.001165508,0.000271737,0.00006986059],"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":[0.00005501976,0.0001811767,0.00001872223,0.00004285814,0.0001511444,1.633955e-7,0.0003414207,0.9674652,0.02034004,0.0004599222,0.0001270359,0.01081734],"study_design_scores_gemma":[0.006100247,0.0004570488,0.0002075542,0.0006520855,0.0002227553,0.00003499765,0.0005845816,0.579717,0.4074441,0.0004800625,0.002989878,0.001109692],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00560474,0.00005917959,0.9915359,0.0003598185,0.0003865445,0.0008801311,0.0002910241,0.0002733418,0.0006093081],"genre_scores_gemma":[0.9048979,0.000185753,0.09400746,0.0000153672,0.00001836783,0.0006134621,0.00002912659,0.00006281908,0.0001697589],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8992931,"threshold_uncertainty_score":0.697354,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03190230938534731,"score_gpt":0.2768430419197617,"score_spread":0.2449407325344144,"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."}}