{"id":"W2159125213","doi":"10.1109/twc.2007.05415","title":"Estimation of Ricean K parameter and local average SNR from noisy correlated channel samples","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Estimator; Fading; Channel (broadcasting); Statistics; Signal-to-noise ratio (imaging); Algorithm; Computer science; Estimation theory; Noise (video); Mathematics; Telecommunications; Artificial intelligence","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":[0.001841942,0.000988605,0.001336461,0.0005472474,0.0002390264,0.000762214,0.0006894867,0.0008515826,0.000521554],"category_scores_gemma":[0.01039163,0.0005015678,0.0004296102,0.0006202855,0.001060083,0.001568628,0.0008944052,0.0005959476,0.0003151024],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003416326,"about_ca_system_score_gemma":0.0006221691,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004215311,"about_ca_topic_score_gemma":0.000669089,"domain_scores_codex":[0.9992164,0.0003548839,0.00004218804,0.0001440539,0.0001897094,0.00005265971],"domain_scores_gemma":[0.9965941,0.002311847,0.0004983693,0.0002765179,0.000281545,0.00003755255],"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":[0.0007416475,0.00006890139,0.00337668,0.0004092805,0.0001737714,0.0003596696,0.0002367436,0.7543906,0.03297169,0.02156231,0.0006425707,0.1850661],"study_design_scores_gemma":[0.00002503121,0.0001016619,0.0008037646,0.00002414484,0.00003399737,0.0002278793,0.00002809446,0.9761137,0.01694851,0.005206033,0.0004433868,0.00004378492],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02492379,0.000268114,0.974232,0.00003412551,0.00001188979,0.00001253682,0.0000199529,0.0001200771,0.0003775026],"genre_scores_gemma":[0.5800431,0.0009656465,0.4172476,0.00006309219,0.00009030206,0.00009711262,0.0001457633,0.00006396808,0.001283449],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001841942,"threshold_uncertainty_score":0.009741247,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02243335224231759,"score_gpt":0.2628986781036509,"score_spread":0.2404653258613333,"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."}}