{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001417206,0.001188052,0.001205272,0.0003726103,0.0003709661,0.0007861456,0.001787539,0.001839612,0.002035999],"category_scores_gemma":[0.004570134,0.0007661913,0.0006370051,0.0004963285,0.0008734805,0.001066434,0.001290384,0.002221249,0.0005006416],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000833954,"about_ca_system_score_gemma":0.001082674,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007517896,"about_ca_topic_score_gemma":0.009981958,"domain_scores_codex":[0.9995801,0.0001680548,0.00002317421,0.00008183729,0.00009330945,0.00005363465],"domain_scores_gemma":[0.9986175,0.0009544449,0.00008061423,0.00008751168,0.000215661,0.00004418685],"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":[0.00003027589,0.0000226024,0.000239799,0.00003929692,0.00003392666,0.00002552736,0.00001909039,0.9742469,0.0005272467,0.004868424,0.00079588,0.01915094],"study_design_scores_gemma":[0.000001548206,0.000006045332,0.00001698083,0.000002838456,0.000001433904,0.000003025035,0.000001030924,0.9985237,0.00009077961,0.001245123,0.0001061938,0.00000141815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01199148,0.0006339795,0.984705,0.0002674462,0.00008840012,0.00003105853,0.00008108276,0.000441618,0.001759883],"genre_scores_gemma":[0.7760826,0.0006714612,0.2134224,0.0005464608,0.0001343421,0.0002400248,0.0004662609,0.0002404408,0.008195885],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007517896,"threshold_uncertainty_score":0.01494831,"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."}}