{"id":"W2246469622","doi":"10.1049/iet-com.2014.0318","title":"Adaptive transmission in amplify‐and‐forward cooperative communications using orthogonal space–time block codes under spatially correlated antennas","year":2015,"lang":"en","type":"article","venue":"IET Communications","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Rayleigh fading; Cumulative distribution function; Spectral efficiency; Spatial correlation; Computer science; Transmission (telecommunications); Algorithm; Fading; Relay; Block code; Coding (social sciences); Bit error rate; Mathematics; Topology (electrical circuits); Probability density function; Channel (broadcasting); Telecommunications; Statistics; Decoding methods; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001025417,0.0005509473,0.0003963749,0.0002593164,0.0002819045,0.0004765565,0.0003760233,0.0004491818,0.0003353546],"category_scores_gemma":[0.004166327,0.0002243984,0.0002700465,0.0006547325,0.0006563619,0.0008623974,0.0005862419,0.0003092528,0.00008405004],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004783791,"about_ca_system_score_gemma":0.0005157581,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001506769,"about_ca_topic_score_gemma":0.001931719,"domain_scores_codex":[0.999225,0.0003148042,0.00002656909,0.00007507896,0.000278268,0.0000802533],"domain_scores_gemma":[0.9960405,0.003060441,0.0004029549,0.0001486233,0.000319637,0.00002782705],"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":[0.0001949735,0.00004713911,0.001608257,0.0001055611,0.00008491726,0.0003153265,0.0002157596,0.9260992,0.02497467,0.01711203,0.0002127081,0.02902948],"study_design_scores_gemma":[0.00001096854,0.0001276911,0.0004202145,0.000007402761,0.00004254769,0.00014359,0.00003740058,0.9898008,0.007423026,0.001730592,0.00024478,0.00001097177],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.444254,0.0008271913,0.5508473,0.0001312689,0.00002141876,0.00003558206,0.00002873888,0.0001192981,0.003735275],"genre_scores_gemma":[0.9854359,0.0003340898,0.01364652,0.00001962342,0.00000941831,0.00001867864,0.000009951094,0.00000641197,0.0005193572],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001506769,"threshold_uncertainty_score":0.005423009,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1127693863978556,"score_gpt":0.3243417703368462,"score_spread":0.2115723839389906,"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."}}