{"id":"W4236709909","doi":"10.1002/wcm.601","title":"Performance of selective space‐time coding and selection diversity under perfect and imperfect CSI","year":2008,"lang":"en","type":"article","venue":"Wireless Communications and Mobile Computing","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Space–time block code; Computer science; Diversity gain; Imperfect; MIMO; Antenna diversity; Cooperative diversity; Channel state information; Transmit diversity; Coding (social sciences); Block code; Selection (genetic algorithm); Coding gain; Transmitter; Telecommunications; Channel (broadcasting); Transmission (telecommunications); Antenna (radio); Algorithm; Fading; Decoding methods; Wireless; Mathematics; Statistics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002041321,0.0001685031,0.0002683246,0.0001351906,0.00106855,0.00001793691,0.0002667624,0.00008078946,0.000001906972],"category_scores_gemma":[0.000009580032,0.0001902599,0.00002640724,0.0002644005,0.0003575886,0.000224071,0.0009509238,0.0003035115,6.342167e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006479296,"about_ca_system_score_gemma":0.00001498595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005402255,"about_ca_topic_score_gemma":0.000009863425,"domain_scores_codex":[0.9992327,0.0000938745,0.0002336497,0.0001775443,0.00008577329,0.0001764486],"domain_scores_gemma":[0.9990171,0.0002889734,0.00009695622,0.0004264427,0.0001095674,0.00006102341],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007884361,0.0003465736,0.3244475,0.0009402916,0.0004416667,0.000001690339,0.02283837,0.03651803,0.2796446,0.006852892,0.0002063298,0.3276832],"study_design_scores_gemma":[0.0003242277,0.0001542798,0.03579888,0.0001441723,0.00002441126,0.00008275529,0.0002725562,0.9502648,0.01246961,0.00003757521,0.0001523903,0.0002743695],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9878294,0.002075135,0.008923011,0.00002050895,0.00001090833,0.0002767557,0.000003697754,0.0003198493,0.0005406815],"genre_scores_gemma":[0.9721782,0.0180217,0.009713321,0.000008556938,0.000009591675,0.00002206296,0.00000697706,0.00002451324,0.00001512303],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9137468,"threshold_uncertainty_score":0.8218531,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01386884617072116,"score_gpt":0.2331598624733143,"score_spread":0.2192910163025932,"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."}}