{"id":"W4292230852","doi":"10.1109/icc45855.2022.9839255","title":"A Proportional Fairness-based Power Allocation Scheme for Non-orthogonal Multiple Access Downlink Systems","year":2022,"lang":"en","type":"article","venue":"ICC 2022 - IEEE International Conference on Communications","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Karush–Kuhn–Tucker conditions; Telecommunications link; Computer science; Noma; Mathematical optimization; Max-min fairness; Transmitter power output; Single antenna interference cancellation; Power (physics); Fairness measure; Interference (communication); Transmitter; Throughput; Constraint (computer-aided design); Optimization problem; Computer network; Channel (broadcasting); Wireless; Resource allocation; Mathematics; Algorithm; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003714914,0.0002783196,0.0002543112,0.0004557364,0.0007234468,0.0001935195,0.004373922,0.0001049886,0.0004423232],"category_scores_gemma":[0.0001654191,0.0003291773,0.0001362922,0.0003878425,0.0002012146,0.0004334273,0.0006694621,0.0007808289,0.00003816846],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005675016,"about_ca_system_score_gemma":0.0002232347,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002410721,"about_ca_topic_score_gemma":0.00005606023,"domain_scores_codex":[0.9979539,0.0001037529,0.0006601001,0.0003552185,0.0006421102,0.0002849487],"domain_scores_gemma":[0.9965349,0.0005210061,0.0002957887,0.001987045,0.0005907489,0.00007054467],"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":[0.0001564568,0.0008170349,0.001411834,0.00007156107,0.0002809392,0.000002114703,0.0002340272,0.6113373,0.02175952,0.3503551,0.008368912,0.005205215],"study_design_scores_gemma":[0.0008311612,0.0000893634,0.0008357613,0.00005692318,0.0000100072,0.000006513558,0.0005482431,0.9578447,0.001456994,0.003303924,0.03461847,0.0003979447],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0664018,0.0007568731,0.8710216,0.02065557,0.004251582,0.004581351,0.004068604,0.003210829,0.02505173],"genre_scores_gemma":[0.9811626,0.0002271784,0.008652805,0.0001744121,0.00004433817,0.007201756,0.002232035,0.00006131798,0.000243528],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9147608,"threshold_uncertainty_score":0.999916,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1051027938792192,"score_gpt":0.3585020419290549,"score_spread":0.2533992480498357,"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."}}