{"id":"W2169342078","doi":"10.1109/pacrim.1995.519544","title":"Maximal ratio combining with channel estimation errors","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Channel (broadcasting); Imperfect; Algorithm; Bit error rate; Weighting; Mathematics; Rayleigh fading; Ideal (ethics); Computer science; Signal-to-noise ratio (imaging); Control theory (sociology); Statistics; Fading; Telecommunications; Acoustics; Physics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00002945715,0.00008433712,0.000076168,0.00005499312,0.00004884032,0.0000159449,0.0001147968,0.00003148166,0.0001356195],"category_scores_gemma":[0.000006213541,0.00007747363,0.00001095075,0.0001205527,0.00002243498,0.0002459858,0.00001637394,0.00009696739,0.00005188964],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003141021,"about_ca_system_score_gemma":0.000001179399,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000291522,"about_ca_topic_score_gemma":0.000005297901,"domain_scores_codex":[0.9996343,0.000007473784,0.0001081299,0.00006773959,0.00007600179,0.0001063983],"domain_scores_gemma":[0.9996544,0.00002087064,0.000018759,0.0002562382,0.00002184341,0.00002793972],"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":[0.000006322919,0.00006159271,0.000139295,0.00005415894,0.00003344165,0.000003964117,0.001312642,0.9152062,0.002366582,0.02015615,0.003666909,0.05699275],"study_design_scores_gemma":[0.0001249392,0.00002690298,0.0001057703,0.00001863414,0.000002530014,0.000005397842,0.00004633617,0.9818674,0.01678344,0.0005011109,0.0003957678,0.0001217959],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008601503,0.0000950349,0.9587787,0.0001354507,0.00002125019,0.0001147282,6.341724e-7,0.00183395,0.03041876],"genre_scores_gemma":[0.8887887,0.00006497099,0.1107855,0.0000332478,0.000005480025,0.00004122661,0.000006057167,0.00002343013,0.000251427],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8801872,"threshold_uncertainty_score":0.3159283,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01969825352572325,"score_gpt":0.2253387500220128,"score_spread":0.2056404964962896,"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."}}