{"id":"W4230023159","doi":"10.32920/ryerson.14661888.v1","title":"Optimizing Macro and Micro Base Stations Locations and User Associations in Heterogeneous Wireless Networks","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Heterogeneous network; Computer science; Base station; Channel state information; Interference (communication); User equipment; Macro; Cellular network; Poisson point process; Computer network; Channel (broadcasting); Wireless network; Wireless; Distributed computing; Telecommunications; Point process; Mathematics","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":[0.0007010865,0.0006615017,0.0006281507,0.0003239558,0.0004477425,0.000817736,0.0005755048,0.0004110376,0.0005788744],"category_scores_gemma":[0.001843806,0.0004398675,0.0003218594,0.0005516778,0.0003723293,0.0008316827,0.0008112646,0.0003766181,0.0002257674],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005532398,"about_ca_system_score_gemma":0.0009677265,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002188894,"about_ca_topic_score_gemma":0.003148198,"domain_scores_codex":[0.9996684,0.0001122513,0.0000142062,0.00007526758,0.00005738772,0.0000725617],"domain_scores_gemma":[0.9995537,0.0002436642,0.00008032082,0.00003719036,0.00005876458,0.00002632786],"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":[0.00003077432,0.000034782,0.001180408,0.00003691186,0.0000181375,0.00006401703,0.00005348349,0.952514,0.006524925,0.005657956,0.0002953736,0.03358921],"study_design_scores_gemma":[0.000003248272,0.00003797059,0.0004009989,0.000004638438,0.00001263456,0.0000228281,0.00004895685,0.9954185,0.001635206,0.002053937,0.0003558874,0.000005202668],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08801524,0.0004214058,0.9086733,0.00009327365,0.00002250666,0.00003810551,0.00002945739,0.0001327669,0.002573908],"genre_scores_gemma":[0.8393674,0.000881278,0.1571056,0.00005738023,0.00002329901,0.0000649366,0.00006041217,0.0000434344,0.002396186],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002188894,"threshold_uncertainty_score":0.004352331,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01024564347750155,"score_gpt":0.2286414506304295,"score_spread":0.218395807152928,"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."}}