{"id":"W2963291364","doi":"10.1109/glocom.2016.7842087","title":"Optimal Joint Power and Subcarrier Allocation for MC-NOMA Systems","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":134,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Subcarrier; Computer science; Resource allocation; Benchmark (surveying); Mathematical optimization; Computational complexity theory; Maximization; Noma; Throughput; Optimization problem; Resource management (computing); Convex optimization; Orthogonal frequency-division multiplexing; Algorithm; Distributed computing; Regular polygon; Wireless; Telecommunications link; Mathematics; Computer network; 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":[0.0009332458,0.0006127529,0.0006782542,0.0003227551,0.0004982812,0.001061576,0.0004792856,0.0005445656,0.0009949574],"category_scores_gemma":[0.003998039,0.0003279627,0.0002400857,0.0006014555,0.0007565839,0.0008782127,0.0005069707,0.000602171,0.0002848165],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006407067,"about_ca_system_score_gemma":0.001164446,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001967633,"about_ca_topic_score_gemma":0.001762126,"domain_scores_codex":[0.999254,0.0003612973,0.00002952831,0.0001010853,0.0001714864,0.00008244195],"domain_scores_gemma":[0.9988086,0.0008136015,0.0001389517,0.00006675514,0.0001423162,0.00002974999],"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":[0.00008673762,0.00005360026,0.0005686312,0.00009616062,0.00003374242,0.00008831586,0.00007384952,0.9107346,0.005185755,0.03747365,0.0007093244,0.04489566],"study_design_scores_gemma":[0.000005604267,0.00001904481,0.00006130762,0.000003750905,0.000003709745,0.00002156657,0.000008954074,0.9951515,0.0008483519,0.003552198,0.0003203565,0.000003550774],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02631733,0.0006658044,0.9701225,0.0001768522,0.00003254279,0.00004944081,0.00002453693,0.0000684663,0.002542616],"genre_scores_gemma":[0.7890288,0.0006107136,0.2082108,0.00008350774,0.00007186901,0.0001136314,0.00003191079,0.00002767,0.001821149],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001967633,"threshold_uncertainty_score":0.004935503,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01503950357914238,"score_gpt":0.221151950516019,"score_spread":0.2061124469368766,"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."}}