{"id":"W4240757515","doi":"10.1109/lcomm.2005.1411012","title":"Maximum likelihood estimation of local average SNR in Ricean fading channels","year":2005,"lang":"en","type":"article","venue":"IEEE Communications Letters","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Estimator; Rician fading; Fading; Mean squared error; Mathematics; Statistics; Minimum-variance unbiased estimator; Bias of an estimator; Efficient estimator; Estimation theory; Probability density function; Applied mathematics; Signal-to-noise ratio (imaging); Minimax estimator; Maximum likelihood; Maximum likelihood sequence estimation","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":[0.001855292,0.0007622198,0.001059101,0.0006792115,0.0002370616,0.0009826196,0.000719864,0.0007438879,0.0007495393],"category_scores_gemma":[0.01072107,0.0004914405,0.0003799123,0.0007116426,0.000920882,0.001544029,0.0009217212,0.0006872182,0.0005527304],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003621779,"about_ca_system_score_gemma":0.0005375389,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005321859,"about_ca_topic_score_gemma":0.0005365336,"domain_scores_codex":[0.999189,0.0004711172,0.00002754483,0.00009154297,0.0001696819,0.0000511117],"domain_scores_gemma":[0.9970111,0.00222121,0.0002925514,0.0001468863,0.0002770333,0.00005130254],"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":[0.0001688289,0.00002608236,0.001479522,0.0001736669,0.00004958256,0.0002075078,0.0001139535,0.8953447,0.007383687,0.02648689,0.001155422,0.06741015],"study_design_scores_gemma":[0.00001087396,0.00002131807,0.0002947694,0.000008714426,0.000005935252,0.00005593861,0.000007671342,0.9874868,0.001488925,0.01037701,0.0002285495,0.00001345957],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01323752,0.0003868674,0.9854509,0.0001005892,0.000007625222,0.000009128556,0.00003161504,0.0002415894,0.0005341914],"genre_scores_gemma":[0.710291,0.001415765,0.2846604,0.0001006767,0.0001402652,0.0001193463,0.0003351817,0.0001719028,0.002765488],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001855292,"threshold_uncertainty_score":0.009811878,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01542057146745428,"score_gpt":0.2597697651700511,"score_spread":0.2443491937025968,"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."}}