{"id":"W2913563820","doi":"10.1109/tcomm.2019.2900634","title":"Energy Efficient Beamforming Design for MISO Non-Orthogonal Multiple Access Systems","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":84,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Engineering and Physical Sciences Research Council","keywords":"Beamforming; Telecommunications link; Mathematical optimization; Computer science; Maximization; Transmitter power output; Spectral efficiency; Efficient energy use; Transmission (telecommunications); Context (archaeology); Noma; WSDMA; Electronic engineering; Mathematics; Precoding; Engineering; Telecommunications; MIMO; Electrical engineering; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001615486,0.0002313485,0.0002553497,0.000304592,0.0004543685,0.00008500004,0.001970311,0.0001574217,0.00001461911],"category_scores_gemma":[0.000009803653,0.0002554776,0.000128518,0.0003833574,0.0001078187,0.0002510451,0.00001904149,0.0003619668,0.00004620017],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001850972,"about_ca_system_score_gemma":0.00003939359,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003439821,"about_ca_topic_score_gemma":0.00004829024,"domain_scores_codex":[0.9988554,0.0000581625,0.0004156139,0.0002128945,0.0001544936,0.0003034287],"domain_scores_gemma":[0.995692,0.001201942,0.00008714987,0.002824627,0.0001309777,0.00006329503],"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":[0.00001329647,0.000119778,0.000006263428,0.00003834467,0.00005774401,6.002377e-8,0.00007756324,0.9810504,0.004554961,0.001124665,0.00009468273,0.01286226],"study_design_scores_gemma":[0.0004905817,0.00004535078,0.00001467227,0.00008281585,0.0000190425,0.000003659843,0.0001955916,0.9587361,0.03217862,0.0001623075,0.007797109,0.0002742035],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004118017,0.0006174678,0.9919029,0.0001605325,0.0004602617,0.0008791983,0.00009563688,0.00105981,0.0007062125],"genre_scores_gemma":[0.9572146,0.001010408,0.0397398,0.00002487361,0.000009685429,0.001692471,0.00003136326,0.00007142965,0.0002053879],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9530966,"threshold_uncertainty_score":0.9999897,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0421727387600164,"score_gpt":0.2749852583077999,"score_spread":0.2328125195477835,"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."}}