{"id":"W3195611087","doi":"10.1109/comst.2021.3104581","title":"Stochastic Geometry Analysis of Spatial-Temporal Performance in Wireless Networks: A Tutorial","year":2021,"lang":"en","type":"article","venue":"IEEE Communications Surveys & Tutorials","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":82,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba; University of Alberta","funders":"","keywords":"Stochastic geometry; Computer science; Spatial correlation; Wireless network; Stochastic geometry models of wireless networks; Context (archaeology); Wireless; Queueing theory; Retransmission; Spatial contextual awareness; Spatial multiplexing; Topology (electrical circuits); Distributed computing; Radio resource management; Computer network; Channel (broadcasting); MIMO; Telecommunications; Geography; Mathematics; Artificial intelligence; Statistics","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":[0.002041756,0.0002209003,0.0007784721,0.0005621414,0.00009355316,0.00003899689,0.0006390589,0.0001847461,0.00003130772],"category_scores_gemma":[0.0003047127,0.0002686557,0.0001375232,0.003427168,0.0001026367,0.0002787616,0.000123815,0.0002675297,0.000006756025],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002329404,"about_ca_system_score_gemma":0.0001115387,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006942124,"about_ca_topic_score_gemma":0.004823125,"domain_scores_codex":[0.9971644,0.001036102,0.001043998,0.0002436831,0.0002231011,0.0002887749],"domain_scores_gemma":[0.9963422,0.0009635357,0.0002405695,0.001982732,0.0004031425,0.00006775111],"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":[0.000007292102,0.00007886698,0.01111589,0.00003443477,0.0003342599,0.000001061239,0.0002005472,0.9847869,0.001444793,0.0002249248,0.00002168482,0.001749356],"study_design_scores_gemma":[0.0006122738,0.00001807586,0.02581174,0.000107822,0.0002739346,0.000001600607,0.00007074686,0.9714584,0.001019473,0.00001822487,0.0002671005,0.000340633],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1682846,0.001099751,0.8256194,0.00001396492,0.004145071,0.0003136382,0.00008495938,0.0001652365,0.0002734171],"genre_scores_gemma":[0.9959131,0.0005301163,0.002419486,0.000002996903,0.0004045738,0.00009425727,0.0005488145,0.00004608363,0.00004061187],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8276285,"threshold_uncertainty_score":0.9999766,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02438778000090019,"score_gpt":0.2665297723978491,"score_spread":0.2421419923969489,"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."}}