{"id":"W2964050133","doi":"10.1109/lwc.2018.2803825","title":"Channel Diagonalization for Cloud Radio Access","year":2018,"lang":"en","type":"article","venue":"IEEE Wireless Communications Letters","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Telecommunications link; Computer science; MIMO; Baseband; Multi-user MIMO; Precoding; Channel (broadcasting); Singular value decomposition; Computer network; Topology (electrical circuits); Electronic engineering; Algorithm; Bandwidth (computing); Engineering; Electrical engineering","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":[0.0001323014,0.0001612064,0.0001685801,0.0001356669,0.0002930566,0.00007922052,0.0009937376,0.00007356078,0.000006473501],"category_scores_gemma":[0.00002236644,0.000189799,0.00005518979,0.0003443495,0.0001606827,0.0004646816,0.00006835585,0.0001044521,0.00003129284],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001376277,"about_ca_system_score_gemma":0.0000122504,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001398664,"about_ca_topic_score_gemma":0.00004636277,"domain_scores_codex":[0.999153,0.0000519349,0.0003055625,0.0001645494,0.00008585754,0.0002391297],"domain_scores_gemma":[0.9983544,0.0001374331,0.0000830131,0.001222449,0.0001475281,0.00005520994],"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":[0.00002871662,0.0000987979,0.0003133281,0.0002492946,0.0001954262,6.759367e-7,0.002503775,0.8647401,0.0508169,0.004640399,0.0671643,0.009248265],"study_design_scores_gemma":[0.0005315913,0.00001837367,0.00009645319,0.00008661926,0.00002817964,0.000005008569,0.00004424665,0.9720187,0.01153806,0.0002047726,0.01505812,0.000369842],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01178533,0.0001597466,0.9841478,0.00124454,0.001109704,0.0005870044,0.00003320589,0.0005216264,0.0004110773],"genre_scores_gemma":[0.9883519,0.0002328568,0.009600877,0.0004805433,0.0005440966,0.0004880276,0.0001759136,0.00008379504,0.00004196252],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9765666,"threshold_uncertainty_score":0.7739778,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03403531581985487,"score_gpt":0.2869369936422904,"score_spread":0.2529016778224356,"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."}}