{"id":"W3164308355","doi":"10.1109/tvt.2021.3082776","title":"Unsupervised Deep Learning Approach for Near Optimal Power Allocation in CRAN","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Deep learning; Artificial intelligence; Computational complexity theory; Cellular network; Face (sociological concept); Distributed computing; Machine learning; Algorithm; Computer network","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00008997376,0.0001796627,0.0002300122,0.0003169262,0.0001251652,0.00002340797,0.0001186565,0.0003729532,0.00002230344],"category_scores_gemma":[0.00001589068,0.000219644,0.00007903714,0.000771968,0.00004875246,0.0001343423,0.000001186789,0.0004405668,0.00001231718],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001452951,"about_ca_system_score_gemma":0.0000238508,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002883587,"about_ca_topic_score_gemma":0.00002026878,"domain_scores_codex":[0.9989986,0.00003260231,0.0002785099,0.0003163289,0.00008443686,0.0002895349],"domain_scores_gemma":[0.9995118,0.00003080688,0.00002778112,0.0002905728,0.0001046195,0.00003444506],"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":[0.00001080374,0.00007505574,0.00001228977,0.00004762597,0.00003673813,0.000006934178,0.0001449606,0.9688902,0.02260595,0.000125866,0.000002511479,0.008041076],"study_design_scores_gemma":[0.0007911408,0.0000657772,0.000008628269,0.00002527389,0.00002032798,0.00003435807,0.000478436,0.9151629,0.08256131,0.00004073053,0.0005997959,0.0002113517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0451983,0.0003002303,0.9529,0.0001000872,0.0001807387,0.0004042239,0.000004191869,0.0007581036,0.0001541103],"genre_scores_gemma":[0.8794736,0.00005534399,0.1199238,0.00001513965,0.000008958133,0.0003645568,0.00002624514,0.00006227905,0.00007005727],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8342753,"threshold_uncertainty_score":0.8956825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007277355301734924,"score_gpt":0.2122699269522055,"score_spread":0.2049925716504706,"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."}}