{"id":"W2158805056","doi":"10.1109/wcnc.2007.376","title":"Minimum Selection GSC with Adaptive Modulation and Post-Combining Power Control","year":2007,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; Norges Forskningsråd; Qatar Foundation","keywords":"Computer science; Spectral efficiency; Power control; Fading; Quadrature amplitude modulation; Diversity combining; Telecommunications link; Transmit diversity; Bit error rate; Transmitter power output; Link adaptation; Electronic engineering; Bandwidth (computing); Interference (communication); Transmission (telecommunications); Signal-to-noise ratio (imaging); Modulation (music); Power (physics); Channel (broadcasting); Telecommunications; Engineering; Transmitter","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007663859,0.0001038777,0.00009303634,0.00007235285,0.00004734072,0.00001449473,0.00001891287,0.00005629825,0.00001920834],"category_scores_gemma":[0.000005741663,0.0000957676,0.000009201933,0.0001629252,0.00001507578,0.0002487254,0.000003677289,0.00007945603,0.000002848013],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005086217,"about_ca_system_score_gemma":0.000004177855,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004594712,"about_ca_topic_score_gemma":0.00002893062,"domain_scores_codex":[0.9995012,0.000006772948,0.0001185295,0.0001183574,0.0000836619,0.0001714372],"domain_scores_gemma":[0.9997476,0.00005242615,0.00002461723,0.00005224201,0.00007812539,0.00004492927],"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":[0.00009625119,0.000006050436,0.001227189,0.000003116284,0.00002349,0.000001150831,0.0001613017,0.9878509,0.004970247,0.0005592221,0.00001668346,0.005084371],"study_design_scores_gemma":[0.0007118396,0.0001339738,0.0149585,0.00001282749,0.000008911556,0.000009553362,0.0001511901,0.9828742,0.0009226359,0.00005702272,0.00002005512,0.0001393479],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2003793,0.0000365413,0.7965844,0.00001174053,0.00005465171,0.0001408282,6.253971e-7,0.0002587787,0.002533103],"genre_scores_gemma":[0.9715595,0.000005658161,0.02825368,0.0000424169,0.00003080782,0.000003105014,0.000006253023,0.00002933165,0.00006924573],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7711802,"threshold_uncertainty_score":0.3905289,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003316023076699606,"score_gpt":0.1835008827620029,"score_spread":0.1801848596853033,"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."}}