{"id":"W4320015699","doi":"10.1109/tcomm.2023.3240393","title":"Cognitive NOMA With Blind Transmission-Mode Identification","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Cognitive radio; Noma; Detector; Underlay; Transmission (telecommunications); Throughput; Algorithm; Transmitter power output; Network packet; Real-time computing; Electronic engineering; Telecommunications link; Computer network; Transmitter; Signal-to-noise ratio (imaging); Telecommunications; Channel (broadcasting); Engineering; Wireless","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":[0.0001114813,0.0002112894,0.000175099,0.0004915951,0.000593152,0.00004988143,0.001050663,0.0001316464,0.000058772],"category_scores_gemma":[0.000008322443,0.0002148293,0.00007410672,0.00153547,0.0002930307,0.0003190745,0.000006487505,0.000592482,0.0002687223],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008828196,"about_ca_system_score_gemma":0.00003261537,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008775755,"about_ca_topic_score_gemma":0.00008384621,"domain_scores_codex":[0.9989222,0.00006833214,0.0003419206,0.0002136611,0.0002006863,0.0002532154],"domain_scores_gemma":[0.9971209,0.0005391851,0.00005847368,0.002072186,0.0001353069,0.00007400814],"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":[0.00009206856,0.0003842312,0.00001592866,0.00006571257,0.0002588909,0.000002962549,0.001589704,0.5135143,0.0176516,0.001334944,0.0004749403,0.4646147],"study_design_scores_gemma":[0.002865887,0.0002111145,0.001146328,0.0005851453,0.0001853216,0.0000316667,0.004334838,0.7086555,0.2615744,0.003276756,0.01582255,0.001310516],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01366453,0.0002578499,0.9776317,0.001246322,0.00009305403,0.0004766546,0.0001125029,0.004217073,0.002300281],"genre_scores_gemma":[0.9868252,0.005541847,0.006260921,0.00002627691,0.000005910882,0.000759167,0.0000953226,0.00007234652,0.0004130271],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9731607,"threshold_uncertainty_score":0.8760485,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03852526292210831,"score_gpt":0.3053131939381385,"score_spread":0.2667879310160303,"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."}}