{"id":"W2792981848","doi":"10.1109/lwc.2018.2815683","title":"Optimal User Grouping for Downlink NOMA","year":2018,"lang":"en","type":"article","venue":"IEEE Wireless Communications Letters","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Telecommunications link; Noma; Computer science; Computational complexity theory; Mathematical optimization; Algorithm; Mathematics; Computer network","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":[0.0001776801,0.0002629601,0.0002656849,0.0002246174,0.0005393452,0.00007448981,0.002796693,0.0001479665,0.00001204949],"category_scores_gemma":[0.00004092336,0.0003022798,0.0001112299,0.0004269043,0.0007537396,0.0003762505,0.0003161924,0.000398292,0.0001069829],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001654033,"about_ca_system_score_gemma":0.00001488153,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009679797,"about_ca_topic_score_gemma":0.0000442579,"domain_scores_codex":[0.9987016,0.00005378263,0.0004384445,0.0002458329,0.0001313013,0.0004290226],"domain_scores_gemma":[0.9953631,0.0003984951,0.0001017915,0.003929515,0.0001442328,0.0000628651],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005212387,0.0002347281,0.0006801884,0.0001909834,0.0004219162,0.00000215725,0.002086638,0.09333668,0.687458,0.03747134,0.03733902,0.1407263],"study_design_scores_gemma":[0.001525745,0.000119816,0.0007299929,0.0002282096,0.00005720192,0.00001734719,0.0007653864,0.5550099,0.1654155,0.0006680707,0.2739364,0.001526433],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2551942,0.0004299625,0.7351294,0.005237147,0.0004411464,0.0005297743,0.0000389694,0.002336046,0.0006634056],"genre_scores_gemma":[0.7960471,0.0004933593,0.2021688,0.0005093957,0.0001129061,0.0005035513,0.00005663925,0.0000805601,0.00002772284],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5408529,"threshold_uncertainty_score":0.999943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02612675654783626,"score_gpt":0.2712134743253921,"score_spread":0.2450867177775559,"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."}}