{"id":"W2753610083","doi":"10.1109/jsyst.2017.2741976","title":"Distributed Transmission Scheduling and Power Allocation in CoMP","year":2017,"lang":"en","type":"article","venue":"IEEE Systems Journal","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Computer science; Orthogonalization; Scheduling (production processes); Precoding; Base station; Distributed computing; Fair-share scheduling; Computer network; Mathematical optimization; Channel (broadcasting); Algorithm; Mathematics; Quality of service; MIMO","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":[0.0003174581,0.0001145255,0.0001862287,0.00009036262,0.0002288986,0.0002257919,0.0001301051,0.00008691871,0.000003152142],"category_scores_gemma":[0.00002626982,0.0001089578,0.00002306983,0.00004263621,0.00001982939,0.0004999786,0.000006026663,0.0002233417,0.00000461032],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001144182,"about_ca_system_score_gemma":0.00001366198,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000169913,"about_ca_topic_score_gemma":0.000005625164,"domain_scores_codex":[0.999218,0.00003697868,0.000354456,0.0001002504,0.0001149705,0.0001753085],"domain_scores_gemma":[0.9995068,0.00001584003,0.0001384702,0.0001755104,0.00006387945,0.0000994427],"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":[0.000005444882,0.000005923492,0.001499759,0.00006846449,0.00001180533,0.00001550348,0.0001820379,0.988003,0.009311066,0.00003443897,0.00006107784,0.0008015101],"study_design_scores_gemma":[0.001232341,0.00002517887,0.005328957,0.001061389,0.000009754481,0.0004903582,0.0001604815,0.9891114,0.00113165,0.00008813181,0.001099566,0.0002608284],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.143313,0.0009707297,0.8538865,0.00004496296,0.001244926,0.0001556325,0.000004289969,0.00005403519,0.0003259424],"genre_scores_gemma":[0.9982263,0.0001220792,0.001448795,0.000001863445,0.0001441488,0.000005841315,0.000003300271,0.00002403323,0.00002363545],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8549133,"threshold_uncertainty_score":0.4443169,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01323437815052538,"score_gpt":0.247599361691801,"score_spread":0.2343649835412756,"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."}}