{"id":"W2496222037","doi":"10.1109/tcomm.2016.2594759","title":"Energy-Efficient Resource Allocation for Downlink Non-Orthogonal Multiple Access Network","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":492,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Southeast University; National Natural Science Foundation of China","keywords":"Telecommunications link; Computer science; Mathematical optimization; Efficient energy use; Resource allocation; Single antenna interference cancellation; Convex optimization; Spectral efficiency; Optimization problem; Maximization; Base station; Noma; Multiplexing; Channel (broadcasting); Algorithm; Mathematics; Regular polygon; Computer network; Engineering; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004940948,0.0004814677,0.0005450642,0.0003103867,0.0004512401,0.0005889488,0.0005368474,0.0003396112,0.0007145436],"category_scores_gemma":[0.00130601,0.0002092886,0.0002752518,0.0005436038,0.0004428952,0.0005709151,0.0005893824,0.0004373861,0.0002194439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005048703,"about_ca_system_score_gemma":0.0007517267,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001500746,"about_ca_topic_score_gemma":0.002410757,"domain_scores_codex":[0.999494,0.0002353747,0.00001718964,0.00006211552,0.0001295515,0.00006191396],"domain_scores_gemma":[0.9996792,0.0001896148,0.00004281137,0.00003075004,0.0000457278,0.00001195298],"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":[0.0001395837,0.0001071288,0.0006903693,0.0001225212,0.00004082585,0.0001690591,0.00007806555,0.8255391,0.01422318,0.02660959,0.002052549,0.1302281],"study_design_scores_gemma":[0.000005559311,0.00002097726,0.00009746262,0.000003039119,0.00000402773,0.00003106465,0.000009595777,0.9943346,0.001445222,0.00355634,0.0004883338,0.000003861583],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02061752,0.0006566504,0.9763088,0.0001363225,0.00002793076,0.00002821506,0.00002014819,0.00009496365,0.002109479],"genre_scores_gemma":[0.8037757,0.0008298281,0.1926728,0.0001115333,0.00004057862,0.0001404551,0.00006558648,0.00003687122,0.002326759],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001500746,"threshold_uncertainty_score":0.003663063,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03135922093522871,"score_gpt":0.2715286323733495,"score_spread":0.2401694114381208,"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."}}