{"id":"W2156370355","doi":"10.1109/lcomm.2005.1496583","title":"NDA estimation of SINR for QAM signals","year":2005,"lang":"en","type":"article","venue":"IEEE Communications Letters","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Estimator; Quadrature amplitude modulation; QAM; Fading; Statistics; Signal-to-noise ratio (imaging); Signal-to-interference-plus-noise ratio; Interference (communication); Mean squared error; Computer science; Moment (physics); Quadrature (astronomy); Amplitude; Mathematics; Algorithm; Modulation (music); Noise (video); Channel (broadcasting); Telecommunications; Bit error rate; Electronic engineering; Acoustics; Physics; Artificial intelligence; Engineering","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":[0.0001613921,0.0001151915,0.0001601822,0.0001365377,0.0001083084,0.00001468567,0.001035086,0.00005227863,0.000008380764],"category_scores_gemma":[0.00003919314,0.000137702,0.00007002369,0.0001868468,0.0001320997,0.0002724202,0.00006330428,0.0001457663,0.00001445021],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009428262,"about_ca_system_score_gemma":0.000009284796,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004780043,"about_ca_topic_score_gemma":0.00001089657,"domain_scores_codex":[0.9992703,0.00004468459,0.0003746258,0.00008407921,0.00008758114,0.0001386842],"domain_scores_gemma":[0.9974597,0.0003827291,0.00009751338,0.001954109,0.00007453658,0.00003146746],"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":[0.000004219579,0.0000631354,0.00001538062,0.00005388457,0.00003825876,3.019038e-8,0.0003698802,0.5562592,0.318407,0.002633478,0.009920826,0.1122347],"study_design_scores_gemma":[0.000276511,0.00001763192,0.00008327699,0.00007616491,0.00002252693,0.00000179099,0.0000304144,0.5897029,0.3568464,0.0006793888,0.05201538,0.0002475996],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03482333,0.0008402413,0.9543242,0.007709332,0.00005533153,0.0005380309,0.00003772738,0.000783142,0.0008886079],"genre_scores_gemma":[0.659295,0.0002965467,0.3398235,0.0002705979,0.00001638825,0.0002175552,0.00004383148,0.00002632375,0.00001035904],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6244716,"threshold_uncertainty_score":0.5615326,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02788013730877713,"score_gpt":0.2985431293833903,"score_spread":0.2706629920746132,"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."}}