{"id":"W2124586345","doi":"10.1109/tcomm.2006.888522","title":"Error Rate of Quadrature Subbranch Hybrid Selection/ Maximal-Ratio Combining in Rayleigh Fading","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Maximal-ratio combining; Quadrature (astronomy); Rayleigh fading; Quadrature amplitude modulation; Diversity combining; Mathematics; Phase-shift keying; Keying; Pulse-amplitude modulation; Binary number; Amplitude and phase-shift keying; Algorithm; Fading; Electronic engineering; Bit error rate; Statistics; Telecommunications; Computer science; Pulse (music); Engineering; Detector; Arithmetic; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006047064,0.0002387921,0.0003086729,0.0006230304,0.0003155062,0.00002642036,0.0009255938,0.0001518635,0.00004812764],"category_scores_gemma":[0.00001601953,0.000293469,0.00009922571,0.001073539,0.0001630035,0.0003813631,0.000008333457,0.001134365,0.00001722092],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002396407,"about_ca_system_score_gemma":0.00003835348,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006131578,"about_ca_topic_score_gemma":0.0008358679,"domain_scores_codex":[0.9984625,0.0001658637,0.0007281675,0.0001907902,0.0001400665,0.0003126339],"domain_scores_gemma":[0.9973205,0.000702402,0.0001197986,0.001640711,0.0001398573,0.00007675125],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001602718,0.001381677,0.0005056869,0.0001677055,0.0002463666,0.000005471896,0.003723218,0.6241257,0.2930106,0.01323436,0.0004385347,0.06300038],"study_design_scores_gemma":[0.0009600828,0.0001019038,0.001114264,0.0002554712,0.00004207739,0.00002821465,0.0005072972,0.1398413,0.8518045,0.001890284,0.002837603,0.0006170448],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02901153,0.0003724396,0.9653873,0.0001908238,0.0001587005,0.0004301007,0.00004026901,0.0007988834,0.003609982],"genre_scores_gemma":[0.9713901,0.00108711,0.02712437,0.00005038069,0.000009401393,0.0001510724,0.00002765001,0.00006672054,0.00009321292],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9423785,"threshold_uncertainty_score":0.9999517,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02448840744955654,"score_gpt":0.2850057485999956,"score_spread":0.260517341150439,"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."}}