{"id":"W2951360543","doi":"10.48550/arxiv.1403.5331","title":"Differential Amplify-and-Forward Relaying in Time-Varying Rayleigh Fading Channels","year":2014,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Fading; Rayleigh fading; Maximal-ratio combining; Algorithm; Signal-to-noise ratio (imaging); Channel (broadcasting); Bit error rate; Mathematics; Computer science; Differential (mechanical device); Fading distribution; Statistics; Topology (electrical circuits); Electronic engineering; Telecommunications; Physics; Engineering; Combinatorics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007441417,0.001163556,0.0005950077,0.0003694718,0.0003202928,0.0007319354,0.0005710283,0.0008060099,0.0003907596],"category_scores_gemma":[0.002280155,0.000192764,0.0003526927,0.0006815075,0.0008462024,0.0008612108,0.0005090015,0.0004183771,0.0001116018],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008532001,"about_ca_system_score_gemma":0.0003731955,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003070003,"about_ca_topic_score_gemma":0.002287013,"domain_scores_codex":[0.9994588,0.0002251123,0.00001820585,0.00006966628,0.0001463735,0.00008174835],"domain_scores_gemma":[0.9985772,0.001037727,0.0001513468,0.00006179167,0.0001477416,0.00002420556],"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":[0.00005906863,0.0000145087,0.0006531823,0.00003672096,0.00002844621,0.0002401565,0.00003688874,0.9814229,0.005810261,0.006381546,0.00006788185,0.005248515],"study_design_scores_gemma":[0.000004562692,0.00005439706,0.0003148232,0.000002933476,0.00001657952,0.00007601783,0.00001636492,0.9955432,0.00214864,0.001711383,0.0001045273,0.000006639704],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5410145,0.00155854,0.4500228,0.0001640861,0.00003859647,0.00003753549,0.00008135114,0.0001513302,0.006931449],"genre_scores_gemma":[0.9924817,0.0005837907,0.006197614,0.00001139841,0.00001247306,0.000008652809,0.00001526852,0.000008073646,0.000681091],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003070003,"threshold_uncertainty_score":0.006190419,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0761536528354059,"score_gpt":0.2103175274744501,"score_spread":0.1341638746390442,"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."}}