{"id":"W2100069164","doi":"10.1109/twc.2007.05098","title":"Maximum likelihood estimation of SNR using digitally modulated signals","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Estimator; Maximum likelihood; Signal-to-noise ratio (imaging); Fading; Algorithm; Computer science; Maximum likelihood sequence estimation; Signal processing; Mean squared error; SIGNAL (programming language); Estimation theory; Statistics; Noise (video); Mathematics; Telecommunications; Artificial intelligence; Decoding methods","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":[0.001956468,0.0009680432,0.001049346,0.0007281649,0.000220405,0.001373762,0.0007615366,0.001190365,0.00072692],"category_scores_gemma":[0.01460944,0.0004899176,0.0004191789,0.0007240121,0.0009082897,0.002207691,0.001027806,0.0007460468,0.0004104878],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004254324,"about_ca_system_score_gemma":0.0005019736,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004863764,"about_ca_topic_score_gemma":0.0003557291,"domain_scores_codex":[0.9990251,0.0005054165,0.00005663001,0.0001364308,0.0002213008,0.00005509552],"domain_scores_gemma":[0.996911,0.0024317,0.0002516943,0.0001532566,0.0002233844,0.00002891376],"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":[0.000524958,0.00007965496,0.002097046,0.00044137,0.0001327252,0.0002917311,0.0001980988,0.6757143,0.03095378,0.06391227,0.0008187191,0.2248353],"study_design_scores_gemma":[0.00003548479,0.00006237398,0.0003499355,0.00002979611,0.00001695488,0.000107407,0.00001272316,0.9737825,0.009353233,0.01560715,0.0006166708,0.00002565446],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01138855,0.0003158299,0.9875186,0.00008931905,0.00001136624,0.00001344275,0.00001927237,0.000129459,0.0005142108],"genre_scores_gemma":[0.4393154,0.001122801,0.5569103,0.0001328176,0.0001349159,0.000119522,0.0002225801,0.00007324245,0.00196835],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001956468,"threshold_uncertainty_score":0.01034689,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02592174453316871,"score_gpt":0.2856968203761756,"score_spread":0.2597750758430069,"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."}}