{"id":"W4254111880","doi":"10.36227/techrxiv.13204007","title":"PRVNet: Variational Autoencoders for Massive MIMO CSI Feedback","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Computer science; MIMO; Channel state information; Telecommunications link; Regularization (linguistics); Channel (broadcasting); Base station; Artificial neural network; Artificial intelligence; Computer engineering; Telecommunications; Wireless","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00006672979,0.0003226574,0.0003521976,0.00008477016,0.00004328639,0.00006575896,0.0002348409,0.0003335677,0.0001489556],"category_scores_gemma":[0.00009062498,0.0003568269,0.0001379635,0.00009648741,0.00001507429,0.000118126,0.0001279163,0.0002959213,0.00007190763],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002503902,"about_ca_system_score_gemma":0.00009515353,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009819275,"about_ca_topic_score_gemma":0.00001069656,"domain_scores_codex":[0.9987519,0.000015479,0.0004118183,0.000419902,0.0001524912,0.0002484207],"domain_scores_gemma":[0.9992516,0.00009347391,0.0001162875,0.0002886886,0.0001489824,0.0001009769],"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":[0.00000742198,0.000006065325,0.000007968165,0.0005134722,0.0001230349,0.000001018912,0.0002170256,0.980581,0.0001546113,0.004331753,0.01381515,0.0002414719],"study_design_scores_gemma":[0.0003559045,0.00001410174,0.00003955846,0.00007449189,0.00003794043,0.000001078045,0.0000530348,0.986946,0.000255571,0.006880628,0.004958129,0.0003835603],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00001222153,0.0001299889,0.9833541,0.0005312379,0.002028171,0.001311565,0.0002186514,0.0009752144,0.01143884],"genre_scores_gemma":[0.04248867,0.00005089011,0.9515131,0.0001698718,0.0009485985,0.0008953665,0.001425104,0.0002167155,0.00229175],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04247645,"threshold_uncertainty_score":0.9998884,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01939020550103363,"score_gpt":0.2404092390818066,"score_spread":0.221019033580773,"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."}}