{"id":"W3044485544","doi":"10.1109/lwc.2020.3011210","title":"Power Minimization for Multi-Cell Uplink NOMA With Imperfect SIC","year":2020,"lang":"en","type":"article","venue":"IEEE Wireless Communications Letters","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Telecommunications link; Computer science; Noma; Imperfect; Single antenna interference cancellation; Mathematical optimization; Quality of service; Linear programming; Optimization problem; Interference (communication); Power (physics); Minification; Computer network; Decoding methods; Telecommunications; Algorithm; Mathematics","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":[0.0006975128,0.0009503025,0.0009548003,0.0003051836,0.0004136561,0.001237663,0.0006643779,0.0007552455,0.00127009],"category_scores_gemma":[0.001457271,0.0004288445,0.0004896088,0.000933702,0.0007651931,0.0007123869,0.0007673666,0.000697326,0.0002750291],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007739732,"about_ca_system_score_gemma":0.0008898591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002488945,"about_ca_topic_score_gemma":0.002521027,"domain_scores_codex":[0.9993222,0.0002804753,0.00001786969,0.00007802688,0.0001899537,0.0001114974],"domain_scores_gemma":[0.9992309,0.0004713473,0.00009731222,0.00004867215,0.000122053,0.00002969367],"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":[0.00004684484,0.00002976884,0.0003110812,0.00006654242,0.00003873298,0.0001123407,0.00003191909,0.9781609,0.002646962,0.00877866,0.0005712252,0.009204994],"study_design_scores_gemma":[0.000005377971,0.00003027902,0.000114584,0.000003170139,0.000006395062,0.00002438255,0.00001219836,0.9970371,0.0005476376,0.002025524,0.0001894067,0.000003912908],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05206959,0.0008866044,0.9376318,0.0003760799,0.00007453581,0.00003951492,0.0001003119,0.0001453537,0.00867627],"genre_scores_gemma":[0.9473726,0.0005117877,0.04907415,0.0001408962,0.00007820135,0.00006161237,0.00006077592,0.00003543145,0.002664607],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002488945,"threshold_uncertainty_score":0.005615532,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03159006945792532,"score_gpt":0.2514037574338976,"score_spread":0.2198136879759723,"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."}}