{"id":"W3112166140","doi":"10.1109/lnet.2020.3045070","title":"Power Allocation in CoMP-Empowered C-NOMA Networks","year":2020,"lang":"en","type":"article","venue":"IEEE Networking Letters","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Fonds de recherche du Québec – Nature et technologies; Concordia University","keywords":"Noma; Computer science; Single antenna interference cancellation; Mathematical optimization; Quality of service; Interference (communication); Power (physics); Heuristic; Computational complexity theory; Optimization problem; Power control; Scheme (mathematics); Transmission (telecommunications); Cellular network; Power optimization; Channel (broadcasting); Computer network; Telecommunications link; Mathematics; Algorithm; Telecommunications","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":[0.00007968433,0.0001799085,0.0002013253,0.00007803293,0.00004875744,0.00003324173,0.0004800902,0.0001116099,0.00001033816],"category_scores_gemma":[0.00001252867,0.0002148436,0.0000425676,0.0005171304,0.00006346923,0.0001382388,0.00005826235,0.000498626,0.00002507258],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000989013,"about_ca_system_score_gemma":0.000004051121,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003106255,"about_ca_topic_score_gemma":0.000007914016,"domain_scores_codex":[0.9990079,0.00003685015,0.0002928717,0.0002099554,0.0001121981,0.0003402171],"domain_scores_gemma":[0.9993747,0.00009855822,0.00005329738,0.0004090395,0.00001196897,0.00005245054],"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":[0.000006765706,0.000004396501,0.001540681,0.000007880568,0.00001345102,0.000008324263,0.0002069299,0.9780099,0.005370901,0.00006609329,0.006907346,0.007857347],"study_design_scores_gemma":[0.0004668197,0.0000213669,0.002334537,0.00009200656,0.000003580544,0.000002711997,0.0000691419,0.9706903,0.001817364,0.00007849732,0.02399474,0.0004289632],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.255125,0.00156032,0.7312731,0.00708034,0.001213482,0.0002985786,0.00000112643,0.002453963,0.0009941307],"genre_scores_gemma":[0.9943843,0.0002590115,0.002581183,0.002467516,0.0002120402,0.0000317891,0.000009175333,0.00005322419,0.0000017775],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7392592,"threshold_uncertainty_score":0.8761069,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01605285362697646,"score_gpt":0.2115908036866127,"score_spread":0.1955379500596363,"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."}}