{"id":"W2126110344","doi":"10.1109/tcomm.2005.861648","title":"Effects of channel-estimation errors on receiver selection-combining schemes for Alamouti MIMO systems with BPSK","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Phase-shift keying; Rayleigh fading; Selection (genetic algorithm); Maximal-ratio combining; Bit error rate; Algorithm; Channel (broadcasting); Diversity combining; Signal-to-noise ratio (imaging); Fading; Mathematics; Diversity gain; MIMO; Statistics; Computer science; Telecommunications; Electronic engineering; Engineering","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":[0.0001229801,0.0002363031,0.000275606,0.0003408662,0.0004187873,0.00002900632,0.0005626499,0.0001277417,0.000003485783],"category_scores_gemma":[0.00001021246,0.0002483127,0.00009187387,0.000521536,0.0001320907,0.0002463845,0.000003380894,0.0003676275,0.000008009707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001826056,"about_ca_system_score_gemma":0.00002808083,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008997491,"about_ca_topic_score_gemma":0.0001004494,"domain_scores_codex":[0.9989194,0.00009639679,0.0004178965,0.0001814945,0.0001732614,0.0002116],"domain_scores_gemma":[0.9975496,0.000860063,0.0001478975,0.001200196,0.0001985068,0.00004375781],"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":[0.00004653207,0.0004155889,0.000007521432,0.0002400921,0.0001072973,1.18687e-7,0.0001939021,0.9802133,0.008069058,0.004867155,0.0002657974,0.005573608],"study_design_scores_gemma":[0.000910623,0.0003141267,0.0001379757,0.0005852708,0.0001102507,0.000005546186,0.0001129654,0.6624761,0.3323474,0.0007816931,0.001829651,0.0003884133],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007101799,0.000324979,0.9890957,0.0001455737,0.0001390889,0.001140076,0.00003912177,0.0009590515,0.001054613],"genre_scores_gemma":[0.9533454,0.000310067,0.04439427,0.00001305497,0.00001214833,0.001642643,0.00004147405,0.00007704393,0.0001639308],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9462436,"threshold_uncertainty_score":0.9999969,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01207034349099351,"score_gpt":0.2455158178500297,"score_spread":0.2334454743590361,"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."}}