{"id":"W2997428863","doi":"10.1002/ett.3842","title":"Edge computing and power control in NOMA‐enabled cognitive radio networks","year":2019,"lang":"en","type":"article","venue":"Transactions on Emerging Telecommunications Technologies","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Mobile edge computing; Computation offloading; Server; Edge computing; Latency (audio); Distributed computing; Computer network; Power control; Computation; Computational complexity theory; Cognitive radio; Edge device; Enhanced Data Rates for GSM Evolution; Power (physics); Cloud computing; Wireless; Algorithm; Artificial intelligence; Telecommunications","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004057789,0.0006110814,0.0005288306,0.000277146,0.000526445,0.0009812602,0.0007065783,0.0003914047,0.0008401129],"category_scores_gemma":[0.0009120539,0.0001834978,0.0003008119,0.0004151218,0.0006789568,0.000535636,0.0007456956,0.0006608268,0.0001282677],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006090211,"about_ca_system_score_gemma":0.0007215284,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004677541,"about_ca_topic_score_gemma":0.00444989,"domain_scores_codex":[0.9996332,0.00009652272,0.00001301523,0.00007743824,0.0000751337,0.0001047873],"domain_scores_gemma":[0.9995961,0.0002117873,0.00005908532,0.00002796372,0.00007443265,0.00003064227],"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":[0.0002307928,0.00008523737,0.0007776928,0.0000950576,0.00003826515,0.0002593809,0.00006884022,0.921773,0.009501582,0.0186327,0.001216737,0.04732069],"study_design_scores_gemma":[0.000004505174,0.00002506772,0.00008709275,0.000002651872,0.00000466087,0.00001468666,0.000009647632,0.9965401,0.000628138,0.002446519,0.000233532,0.000003386812],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07648496,0.001020291,0.9131363,0.0003218288,0.0001461915,0.00003312466,0.00004136565,0.0001602203,0.008655658],"genre_scores_gemma":[0.9840235,0.0002036503,0.01439853,0.00006518116,0.00003063177,0.00001917284,0.00001089141,0.000007446582,0.00124095],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004677541,"threshold_uncertainty_score":0.00930059,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007327789379495654,"score_gpt":0.2364454691007941,"score_spread":0.2291176797212985,"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."}}