{"id":"W2806665931","doi":"10.1109/twc.2019.2946824","title":"Asynchronous Downlink Massive MIMO Networks: A Stochastic Geometry Approach","year":2019,"lang":"en","type":"preprint","venue":"IEEE Transactions on Wireless Communications","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Sharif University of Technology","keywords":"Telecommunications link; Asynchronous communication; Ergodic theory; MIMO; Stochastic geometry; Computer science; Synchronization (alternating current); Transmission (telecommunications); Spectral efficiency; Power control; Wireless; Topology (electrical circuits); Power (physics); Control theory (sociology); Computer network; Mathematics; Telecommunications; Control (management); Physics; Statistics; Channel (broadcasting)","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":[0.0008773337,0.001252216,0.001002362,0.0008440712,0.000472931,0.001203661,0.001054378,0.0009052361,0.002019431],"category_scores_gemma":[0.002266451,0.0006059986,0.0008163525,0.0007517035,0.001077729,0.00116616,0.001180245,0.0009509351,0.000495936],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001600018,"about_ca_system_score_gemma":0.0006894936,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004441395,"about_ca_topic_score_gemma":0.002934923,"domain_scores_codex":[0.9992849,0.0003124241,0.00001736011,0.0001035066,0.0001640161,0.0001177585],"domain_scores_gemma":[0.9984188,0.0008295788,0.0002834901,0.00009436894,0.0002531965,0.0001205652],"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":[0.00003942648,0.00001973584,0.0005473754,0.00004334448,0.0000386657,0.0002159535,0.00003732528,0.8941559,0.001871377,0.09828418,0.001433268,0.003313413],"study_design_scores_gemma":[0.00000468292,0.00001452123,0.0001914724,0.000004584433,0.0000072234,0.00003501091,0.00001158192,0.9839977,0.0002080677,0.01512332,0.0003922919,0.000009535107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05070245,0.001431248,0.9313894,0.001174618,0.0001756614,0.00005344776,0.0004116566,0.0002057776,0.01445567],"genre_scores_gemma":[0.953564,0.002497465,0.03238724,0.000365114,0.0005514324,0.0001234115,0.0003848506,0.0001023046,0.0100242],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004441395,"threshold_uncertainty_score":0.01160896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01894770256795696,"score_gpt":0.2448038565047492,"score_spread":0.2258561539367922,"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."}}