{"id":"W2126417905","doi":"10.1109/ccece.2008.4564590","title":"Throughput enhancement in cooperative diversity wireless networks using adaptive modulation","year":2008,"lang":"en","type":"article","venue":"Conference proceedings - Canadian Conference on Electrical and Computer Engineering","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Throughput; Diversity gain; Computer science; Cooperative diversity; Rayleigh fading; Link adaptation; Fading; Signal-to-noise ratio (imaging); Modulation (music); Diversity combining; Diversity scheme; Computer network; Antenna diversity; Transmission (telecommunications); Electronic engineering; Wireless; Telecommunications; Channel (broadcasting); Engineering; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.001091159,0.0005561861,0.0004081439,0.0005294545,0.0002836867,0.0006224126,0.000383627,0.0004009372,0.0004042833],"category_scores_gemma":[0.00352475,0.0001660247,0.0002299031,0.0006849803,0.0007864652,0.0009072544,0.0004485046,0.000330593,0.0001350587],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008021076,"about_ca_system_score_gemma":0.0002583579,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007610291,"about_ca_topic_score_gemma":0.0005041978,"domain_scores_codex":[0.9990909,0.0003572286,0.00001970439,0.00007303121,0.0003073272,0.0001519602],"domain_scores_gemma":[0.9979728,0.001387429,0.0001773657,0.0001350373,0.0002968556,0.00003058035],"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":[0.0004467378,0.0001044975,0.003357603,0.000140633,0.00008772159,0.000519409,0.0003449876,0.820012,0.08628368,0.02735628,0.000550142,0.06079641],"study_design_scores_gemma":[0.00001534934,0.0003109472,0.001501889,0.00001815988,0.00005373104,0.0002109226,0.00004767644,0.972337,0.01805946,0.006503547,0.0009213994,0.00001994165],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5501295,0.003398214,0.4331902,0.0002956196,0.00006703505,0.00003798488,0.00003671816,0.0003810506,0.01246363],"genre_scores_gemma":[0.9951723,0.0003739236,0.004037189,0.00001421052,0.00002546194,0.000009059223,0.000007481315,0.000007403553,0.00035293],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001091159,"threshold_uncertainty_score":0.005819798,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.051113480108264,"score_gpt":0.224461675608707,"score_spread":0.173348195500443,"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."}}