{"id":"W2739709931","doi":"10.1007/978-3-319-57388-5_6","title":"Resource Allocation in a NOMA-Based VWN","year":2017,"lang":"en","type":"book-chapter","venue":"Springer briefs in electrical and computer engineering","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Noma; Resource allocation; Computer science; Scheme (mathematics); Mathematical optimization; Power (physics); Resource (disambiguation); Transmitter power output; Isolation (microbiology); Computer network; Mathematics; Telecommunications link; Transmitter","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":[0.001143122,0.0007165973,0.001313429,0.0004399968,0.001201743,0.002452844,0.001698992,0.001033723,0.003521408],"category_scores_gemma":[0.002070345,0.0004708471,0.0004682732,0.001123739,0.0007565813,0.001521735,0.001576092,0.001122404,0.0008533058],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008499103,"about_ca_system_score_gemma":0.001071891,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002003794,"about_ca_topic_score_gemma":0.003673801,"domain_scores_codex":[0.999061,0.0003780191,0.0000421917,0.0001842055,0.0001482103,0.0001864406],"domain_scores_gemma":[0.9992676,0.0004250495,0.00003939932,0.00007867868,0.0001372286,0.0000519337],"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":[0.0004128189,0.0001759475,0.0005341519,0.0003087289,0.0001164598,0.0006034642,0.0001685215,0.6573281,0.01400551,0.1448145,0.01340874,0.168123],"study_design_scores_gemma":[0.00001778022,0.00006874902,0.0001076583,0.00002175667,0.00002357559,0.0001853045,0.0000408161,0.9644274,0.000748782,0.03171685,0.002624392,0.00001704144],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03054651,0.002739359,0.9449705,0.0007399406,0.0008833134,0.0001134614,0.0001832715,0.000275248,0.01954841],"genre_scores_gemma":[0.8324854,0.001710332,0.1491842,0.0004039101,0.0005920429,0.0002261674,0.0001434099,0.00008134048,0.01517314],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003521408,"threshold_uncertainty_score":0.01178032,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008685562666442185,"score_gpt":0.1936750702814382,"score_spread":0.184989507614996,"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."}}