{"id":"W2033644422","doi":"10.1109/twc.2011.120611.11006","title":"Amplify-and-Forward Selection Cooperation over Rayleigh Fading Channels with Imperfect CSI","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Rayleigh fading; Relay; Fading; Computer science; Channel (broadcasting); Upper and lower bounds; Imperfect; Bandwidth (computing); Selection (genetic algorithm); Signal-to-noise ratio (imaging); Telecommunications; Relay channel; Algorithm; Channel capacity; Statistics; Mathematics","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","sts"],"consensus_categories":[],"category_scores_codex":[0.000349008,0.0002781907,0.0002541509,0.0003069397,0.001633558,0.0002391193,0.001411537,0.0001228934,0.00007986093],"category_scores_gemma":[0.000006006513,0.0002587523,0.00007274107,0.001044489,0.0002069169,0.0009682539,0.00003889311,0.0006394482,0.00004078179],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001214312,"about_ca_system_score_gemma":0.00009886755,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008234008,"about_ca_topic_score_gemma":0.0004881813,"domain_scores_codex":[0.9982737,0.0004083157,0.0003631447,0.0004303981,0.0002126258,0.0003118479],"domain_scores_gemma":[0.9972214,0.0002785756,0.0001376801,0.001939374,0.0002686446,0.0001543097],"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":[0.0003069571,0.00300484,0.00112242,0.00007496607,0.0008130687,0.000006187419,0.03491832,0.01681396,0.03290163,0.3667105,0.000670703,0.5426565],"study_design_scores_gemma":[0.002260706,0.0009590219,0.001843989,0.0003082115,0.0001384826,0.0001310523,0.0002493924,0.9328812,0.05299932,0.0005272383,0.006323329,0.001378014],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03192902,0.0001759648,0.9642873,0.000660027,0.0001895185,0.000447105,0.000006137117,0.0004234358,0.001881511],"genre_scores_gemma":[0.9777954,0.002794949,0.01846501,0.0003375684,0.00002117145,0.0002910681,0.000009036658,0.00003134461,0.0002544415],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9458664,"threshold_uncertainty_score":0.9999865,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05018551072171268,"score_gpt":0.2727535342234887,"score_spread":0.222568023501776,"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."}}