{"id":"W2097717088","doi":"10.1109/glocom.2007.823","title":"Performance of Decode-and-Forward Cooperative Diversity Networks Over Nakagami-m Fading Channels","year":2007,"lang":"en","type":"article","venue":"","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Fading; Relay; Nakagami distribution; Computer science; Cooperative diversity; Bit error rate; Channel (broadcasting); SIGNAL (programming language); Relay channel; Diversity combining; Antenna diversity; Diversity scheme; Diversity gain; Algorithm; Telecommunications; Electronic engineering; Topology (electrical circuits); Wireless; Electrical engineering; Engineering; Physics","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":[0.002426385,0.001138041,0.0007948993,0.0007032463,0.0006868204,0.001116709,0.0005326,0.001234065,0.0005527181],"category_scores_gemma":[0.011208,0.0002868016,0.0002914834,0.0005081502,0.001501056,0.001229823,0.001173304,0.0004787997,0.000193763],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001311988,"about_ca_system_score_gemma":0.0007097286,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004361659,"about_ca_topic_score_gemma":0.00287844,"domain_scores_codex":[0.9986267,0.0005425222,0.00005933958,0.0001314378,0.0003946786,0.0002454325],"domain_scores_gemma":[0.9891271,0.00824758,0.0007735422,0.0004015532,0.001298683,0.0001515059],"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":[0.0003770669,0.00003968178,0.002500625,0.00006281734,0.00005532098,0.000142891,0.000179788,0.9756846,0.008433299,0.003523021,0.0001518193,0.008849061],"study_design_scores_gemma":[0.00002644128,0.0003319582,0.001365555,0.00001778042,0.00004961514,0.0001501167,0.00007673844,0.985144,0.009661843,0.002990824,0.0001510997,0.00003418031],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8743385,0.001249859,0.1191705,0.0002503059,0.00002600246,0.0000319201,0.0001316007,0.0002537429,0.004547624],"genre_scores_gemma":[0.9958984,0.0002301605,0.003279501,0.00002658286,0.000006914229,0.00001571523,0.00003496998,0.000008780908,0.0004990338],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004361659,"threshold_uncertainty_score":0.01283211,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02913654834944544,"score_gpt":0.2643964965115408,"score_spread":0.2352599481620954,"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."}}