{"id":"W2901382282","doi":"10.1109/tvt.2018.2881314","title":"Joint Energy Efficient Subchannel and Power Optimization for a Downlink NOMA Heterogeneous Network","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":153,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Engineering and Physical Sciences Research Council","keywords":"Macrocell; Computer science; Heterogeneous network; Telecommunications link; Resource allocation; Efficient energy use; Throughput; Spectral efficiency; Mathematical optimization; Cellular network; Transmitter power output; Optimization problem; Computer network; Distributed computing; Wireless network; Wireless; Channel (broadcasting); Base station; Engineering; Algorithm; Telecommunications; Mathematics","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":[0.00006340924,0.0002180661,0.0002317616,0.000375004,0.0002653963,0.00001754242,0.0002491277,0.000413486,0.00002026932],"category_scores_gemma":[0.000008535565,0.0002315353,0.00007158532,0.0004906137,0.0003102095,0.00004486749,0.000006955558,0.0002316939,0.000007984822],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008434756,"about_ca_system_score_gemma":0.000008735981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001975018,"about_ca_topic_score_gemma":0.00001706893,"domain_scores_codex":[0.9990011,0.00001470884,0.0002640531,0.0002903582,0.00008109882,0.0003486634],"domain_scores_gemma":[0.9990989,0.00004248335,0.00005051789,0.0006573148,0.0001112138,0.00003959506],"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":[0.00001408817,0.00003923379,6.000529e-7,0.000009729432,0.0000507468,0.000001830933,0.00002280735,0.9721249,0.003282572,0.001133023,0.0000302118,0.02329031],"study_design_scores_gemma":[0.000340968,0.0002530966,0.000001298318,0.0000274912,0.00001632974,0.00003223884,0.00004162874,0.7830203,0.2131395,0.0009976424,0.001912882,0.000216538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0460494,0.000598634,0.9500782,0.0003950116,0.0003842603,0.0002713856,0.00001540826,0.002170529,0.00003718155],"genre_scores_gemma":[0.9543563,0.0004058869,0.04473658,0.00005695145,0.00002461914,0.0003465289,0.000004665448,0.00005618027,0.00001228193],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9083069,"threshold_uncertainty_score":0.9441738,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009085521901067003,"score_gpt":0.2065781592497714,"score_spread":0.1974926373487044,"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."}}