{"id":"W2911910187","doi":"10.1109/lwc.2019.2895039","title":"Deep Autoencoder Based CSI Feedback With Feedback Errors and Feedback Delay in FDD Massive MIMO Systems","year":2019,"lang":"en","type":"article","venue":"IEEE Wireless Communications Letters","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":91,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Channel state information; Autoencoder; MIMO; Telecommunications link; Feedback loop; Control theory (sociology); Channel (broadcasting); Transmission (telecommunications); Deep learning; Artificial intelligence; Wireless; Telecommunications","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":[0.0007243272,0.0006250624,0.0006634222,0.0001270476,0.0002224511,0.0004440105,0.0004987617,0.0006942036,0.0006415726],"category_scores_gemma":[0.00155332,0.0003669536,0.0002863439,0.0002169785,0.0006532111,0.0007540463,0.000557741,0.0008821692,0.00008721626],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005699212,"about_ca_system_score_gemma":0.0007635436,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006402398,"about_ca_topic_score_gemma":0.004828491,"domain_scores_codex":[0.9997213,0.0000833934,0.00001274712,0.00005258723,0.00007413619,0.00005571614],"domain_scores_gemma":[0.9993644,0.0003979648,0.00006403462,0.00003852628,0.0001134192,0.00002162817],"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":[0.00006270384,0.00001929329,0.0004504398,0.0000448921,0.00002621789,0.00007333639,0.00003644461,0.9808874,0.002300331,0.003623732,0.0002229511,0.01225215],"study_design_scores_gemma":[0.000001851386,0.00001180293,0.00005694835,0.00000179491,0.000002971354,0.000007974417,0.00000240225,0.9991471,0.0003259932,0.0003982073,0.00004111544,0.000001879071],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1064513,0.0008101405,0.8900129,0.0003049153,0.00008652301,0.00001947432,0.00005474097,0.0001757254,0.0020842],"genre_scores_gemma":[0.9797458,0.0002640225,0.01816077,0.00007169202,0.00003329461,0.00001580099,0.00003090678,0.00001247359,0.001665311],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006402398,"threshold_uncertainty_score":0.0127303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01003808143852712,"score_gpt":0.2140095870209564,"score_spread":0.2039715055824293,"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."}}