{"id":"W2167639215","doi":"10.1109/tsp.2008.2011829","title":"Large-System-Based Performance Analysis and Design of Multiuser Cooperative Networks","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University; Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"","keywords":"Relay; Computer science; Interference (communication); Signal-to-noise ratio (imaging); Noise (video); Channel (broadcasting); Code division multiple access; Computer network; Signal-to-interference-plus-noise ratio; Noise power; Relay channel; Channel state information; Multiuser detection; Power (physics); Telecommunications; Wireless; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002525782,0.001035693,0.000765355,0.0004118872,0.0004028637,0.001153101,0.001214526,0.001014583,0.001363975],"category_scores_gemma":[0.007342101,0.0005074213,0.0003486755,0.0005099769,0.001043171,0.001212938,0.00103284,0.0008378825,0.0003423619],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001432293,"about_ca_system_score_gemma":0.0008889324,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001894866,"about_ca_topic_score_gemma":0.001653154,"domain_scores_codex":[0.9985356,0.0007707277,0.0000297256,0.0001667016,0.0003955319,0.0001016074],"domain_scores_gemma":[0.9977829,0.001426946,0.0001939271,0.0001475252,0.0004058525,0.00004288407],"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":[0.00001303982,0.00001035533,0.0001587602,0.00003467316,0.00001437401,0.00003691305,0.00003807623,0.9819462,0.001399142,0.01078211,0.0001821134,0.005384204],"study_design_scores_gemma":[0.000001771488,0.00001159206,0.00003517883,0.000002379187,0.000002605658,0.000008231373,0.000005140181,0.9968349,0.000225965,0.002721263,0.0001486329,0.000002318729],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0179639,0.000452947,0.9759042,0.0001654095,0.00002150612,0.00004180644,0.00002157522,0.0001510351,0.005277685],"genre_scores_gemma":[0.9469731,0.00061977,0.05024431,0.00009238779,0.00005648899,0.0001619891,0.00004996435,0.00006062892,0.001741264],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002525782,"threshold_uncertainty_score":0.01335776,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03063873369173471,"score_gpt":0.2673074275788639,"score_spread":0.2366686938871292,"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."}}