{"id":"W2019099392","doi":"10.1109/tcomm.2006.869806","title":"M-ary NCFSK with S+N selection combining in Rician fading","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Rician fading; Fading; Fading distribution; Multipath propagation; Algorithm; Diversity combining; Independent and identically distributed random variables; Mathematics; Phase-shift keying; Signal-to-noise ratio (imaging); Bit error rate; Electronic engineering; Telecommunications; Statistics; Computer science; Rayleigh fading; Engineering; Random variable; Decoding methods; Estimator","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":[0.001456694,0.0008004358,0.000903777,0.0006703802,0.0006753645,0.0006933114,0.0005647896,0.0007682823,0.0007293713],"category_scores_gemma":[0.002816628,0.0002243796,0.00040903,0.001361061,0.001016522,0.0009336433,0.0006845157,0.0002766153,0.000318471],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009210843,"about_ca_system_score_gemma":0.0009301064,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004228265,"about_ca_topic_score_gemma":0.005259537,"domain_scores_codex":[0.9987753,0.0003364237,0.00008522844,0.0001008175,0.0005081647,0.0001940476],"domain_scores_gemma":[0.9978343,0.0009722474,0.0004905636,0.000200818,0.0004443774,0.00005766578],"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":[0.002221518,0.0001482228,0.009338676,0.0003878463,0.0002903284,0.0006767673,0.0004530838,0.7578551,0.06285047,0.01521318,0.0008660148,0.1496989],"study_design_scores_gemma":[0.0001264176,0.000837839,0.002550852,0.00003484064,0.0001289583,0.0007636217,0.00008482541,0.9515249,0.0379449,0.004384145,0.001548215,0.00007057239],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6856795,0.001325556,0.298428,0.0002317323,0.00005608186,0.0001006467,0.00009687534,0.0005150561,0.01356651],"genre_scores_gemma":[0.9689816,0.0003037567,0.0294361,0.00003676741,0.00002632718,0.000027809,0.00004133291,0.000005997577,0.001140299],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004228265,"threshold_uncertainty_score":0.008407295,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01368594157481975,"score_gpt":0.2420516088798373,"score_spread":0.2283656673050175,"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."}}