{"id":"W4297899891","doi":"10.48550/arxiv.2209.10332","title":"Deep Learning for Multi-User MIMO Systems: Joint Design of Pilot, Limited Feedback, and Precoding","year":2022,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Science and ICT, South Korea; National Research Foundation of Korea; National Research Foundation","keywords":"Precoding; MIMO; Computer science; Telecommunications link; Channel state information; Channel (broadcasting); Base station; Scalability; Artificial neural network; Computer engineering; Control theory (sociology); Artificial intelligence; Computer network; Telecommunications; Wireless","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007252055,0.0008817196,0.0006801073,0.0001576479,0.0002083623,0.0004799656,0.0007624584,0.0008872024,0.0007956059],"category_scores_gemma":[0.001583677,0.0004718672,0.0002856663,0.0002570461,0.000712936,0.0008789733,0.0008084988,0.001033068,0.0001569416],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007174418,"about_ca_system_score_gemma":0.001164089,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004022566,"about_ca_topic_score_gemma":0.005348611,"domain_scores_codex":[0.9997163,0.00008732378,0.00001174709,0.0000560436,0.00007617808,0.00005237051],"domain_scores_gemma":[0.9996332,0.0001874837,0.00004262429,0.00002632004,0.00008634972,0.00002395036],"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":[0.00005343714,0.00003789257,0.0003505384,0.0000511711,0.00002200672,0.00003707315,0.00003149534,0.9556749,0.002645981,0.003184975,0.0003660177,0.03754443],"study_design_scores_gemma":[0.000002620736,0.00001786119,0.0000221962,0.000001793891,0.000002197732,0.000003493893,0.000001945205,0.9989356,0.000404174,0.0005310393,0.00007587016,0.00000119968],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02415804,0.0003387692,0.9735317,0.0001735453,0.00002515869,0.00002681361,0.00001786296,0.0002054349,0.001522673],"genre_scores_gemma":[0.867258,0.0003293182,0.1294723,0.0001940594,0.00004158447,0.0001290467,0.00006029624,0.00003781261,0.002477753],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004022566,"threshold_uncertainty_score":0.007998288,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1087561756012983,"score_gpt":0.1979971486995437,"score_spread":0.08924097309824543,"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."}}