{"id":"W2334664297","doi":"10.1109/tvt.2015.2417568","title":"Closed-Form Average SNR and Ergodic Capacity Approximations for Best Relay Selection","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Ergodic theory; Relay; Cumulative distribution function; Signal-to-noise ratio (imaging); Probability density function; Mathematics; Expression (computer science); Selection (genetic algorithm); Topology (electrical circuits); Capacity planning; Applied mathematics; Function (biology); Power (physics); Statistical physics; Mathematical optimization; Computer science; Statistics; Mathematical analysis; Physics; 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":[0.003699812,0.001619721,0.001401234,0.00149273,0.0006276889,0.002078521,0.001759462,0.001161455,0.003178041],"category_scores_gemma":[0.02193517,0.0005802535,0.0009366834,0.001712372,0.002389854,0.003484638,0.001508826,0.001864732,0.001096802],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002460163,"about_ca_system_score_gemma":0.001355434,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003437366,"about_ca_topic_score_gemma":0.002710368,"domain_scores_codex":[0.9981494,0.0006530444,0.00006654137,0.0002190838,0.0006218475,0.000290165],"domain_scores_gemma":[0.9891703,0.008330649,0.0005335631,0.0006058072,0.001241802,0.0001178463],"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":[0.00004022651,0.00002359613,0.0003476001,0.0001409047,0.00002900929,0.000176178,0.0001310905,0.9105892,0.001532074,0.07440596,0.001569737,0.01101457],"study_design_scores_gemma":[0.00000296639,0.00001204632,0.0001183112,0.00003120175,0.000009128971,0.0001089439,0.00003799435,0.9767026,0.000527144,0.02182181,0.0006159571,0.00001183108],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01523409,0.002002301,0.9713245,0.0002801928,0.00006006677,0.00003839027,0.0001469009,0.0003266261,0.01058682],"genre_scores_gemma":[0.8546482,0.005428838,0.1323699,0.0003763636,0.000217762,0.0003042365,0.0004055511,0.0003422264,0.005906871],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003699812,"threshold_uncertainty_score":0.01956671,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04707732527524373,"score_gpt":0.2665974517169061,"score_spread":0.2195201264416624,"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."}}