{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001498141,0.0009452393,0.0006884778,0.0008114599,0.0003562807,0.0009041154,0.0005134913,0.000525976,0.0009767602],"category_scores_gemma":[0.009409335,0.0003325944,0.0004054947,0.0004622738,0.0005415365,0.0009711966,0.0007916466,0.0005232547,0.000477102],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005734379,"about_ca_system_score_gemma":0.0007041365,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001065188,"about_ca_topic_score_gemma":0.001770254,"domain_scores_codex":[0.9993064,0.0003418346,0.00003128633,0.00009437378,0.0001725471,0.00005354505],"domain_scores_gemma":[0.9972399,0.001870498,0.0001823419,0.0002131813,0.0004569462,0.00003707873],"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":[0.0003605475,0.00006202051,0.005198159,0.0004565221,0.0001102084,0.0002701874,0.0001788227,0.7089102,0.05771209,0.05632175,0.001184954,0.1692344],"study_design_scores_gemma":[0.000005819268,0.00003657649,0.0006754437,0.0000157195,0.000009390009,0.00007535193,0.00001089262,0.987604,0.005601684,0.005403984,0.000549433,0.00001161537],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02087765,0.0005362283,0.9767122,0.0001157229,0.00005884687,0.00002298351,0.00006282405,0.0001361266,0.001477312],"genre_scores_gemma":[0.6608946,0.001519578,0.332162,0.0001186179,0.0001971165,0.0001292733,0.0003585196,0.00008166376,0.004538598],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001498141,"threshold_uncertainty_score":0.007923067,"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."}}