{"id":"W2126195483","doi":"10.1109/tcomm.2003.813183","title":"On the SNR penalty of MPSK with hybrid selection/maximal ratio combining over i.i.d. rayleigh fading channels","year":2003,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":78,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Maximal-ratio combining; Upper and lower bounds; Signal-to-noise ratio (imaging); Mathematics; Rayleigh fading; Modulation (music); Diversity combining; Phase-shift keying; Selection (genetic algorithm); Fading; Algorithm; Telecommunications; Statistics; Control theory (sociology); Computer science; Bit error rate; Physics; Mathematical analysis; Acoustics; 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":[],"consensus_categories":[],"category_scores_codex":[0.0002887408,0.0002528925,0.0002492855,0.0002412841,0.0007770864,0.00004615283,0.0009011489,0.00007153039,0.0001699768],"category_scores_gemma":[0.00002327851,0.0002167895,0.0001048013,0.0006509821,0.0002395723,0.000260247,0.000005836856,0.0008336591,0.00002187467],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001585797,"about_ca_system_score_gemma":0.00005157015,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001828999,"about_ca_topic_score_gemma":0.00005307481,"domain_scores_codex":[0.9986351,0.0002705858,0.0004200186,0.0001839165,0.0002439558,0.0002464972],"domain_scores_gemma":[0.9965125,0.001042536,0.0001296426,0.002094824,0.0001554247,0.00006508335],"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":[0.0000558538,0.0006564563,0.00003279905,0.00004088892,0.0003046922,7.172666e-7,0.001157014,0.8833742,0.01450131,0.09512891,0.0007179942,0.0040292],"study_design_scores_gemma":[0.001103299,0.0004082104,0.00008867426,0.00034538,0.0001163412,0.00005672572,0.0004091198,0.1942252,0.7905523,0.006218614,0.005719446,0.0007566531],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02546766,0.0001330382,0.9640996,0.0003587296,0.0001219541,0.0005676492,0.00003446426,0.0006580447,0.008558895],"genre_scores_gemma":[0.9868748,0.0006629299,0.01167392,0.0000966852,0.000006664992,0.0004541422,0.000009487952,0.00007173447,0.0001495854],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9614072,"threshold_uncertainty_score":0.884042,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02369243854555452,"score_gpt":0.2532405879033361,"score_spread":0.2295481493577816,"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."}}