{"id":"W2885474230","doi":"10.1109/icc.2018.8422363","title":"Dynamic Resource Allocation for Uplink MIMO NOMA VWN with Imperfect SIC","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Telecommunications link; Geometric programming; Computer science; MIMO; Noma; Resource allocation; Mathematical optimization; Single antenna interference cancellation; Spectral efficiency; Convex optimization; Interference (communication); Iterative method; Algorithm; Regular polygon; Computer network; Mathematics; Decoding methods; Channel (broadcasting)","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":[],"consensus_categories":[],"category_scores_codex":[0.00005664408,0.0001128165,0.0001011362,0.00007594748,0.00008371428,0.00001635028,0.0002985706,0.00007584214,0.00001615858],"category_scores_gemma":[0.00003008998,0.00009651801,0.00002186442,0.0001699931,0.0001015134,0.0001061879,0.00004469866,0.00009074915,0.00003901702],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007176065,"about_ca_system_score_gemma":0.000007541947,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002905527,"about_ca_topic_score_gemma":0.00006445128,"domain_scores_codex":[0.9995134,0.00000509862,0.0001247068,0.0001319561,0.00005244857,0.0001723792],"domain_scores_gemma":[0.9991788,0.00008595473,0.00002632491,0.0006256088,0.00006237298,0.0000209912],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001652137,0.0001157072,0.0006489258,0.0002972057,0.0002498367,0.0000013985,0.0008602542,0.1106512,0.139409,0.06620444,0.006380467,0.6750164],"study_design_scores_gemma":[0.000632705,0.0003646112,0.0005971931,0.00004604384,0.00001312215,0.000006993209,0.0004609453,0.8789743,0.09051298,0.002355204,0.02563889,0.0003969982],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09352911,0.0001837896,0.8969249,0.0003883433,0.000033639,0.0003114198,0.000002214985,0.00211645,0.006510203],"genre_scores_gemma":[0.9084392,0.00004008538,0.09096702,0.00003471458,0.00001447326,0.0001176484,0.00001845179,0.00003885792,0.0003295786],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8149101,"threshold_uncertainty_score":0.393589,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00790856373533041,"score_gpt":0.2346595255193106,"score_spread":0.2267509617839802,"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."}}