{"id":"W2344910467","doi":"10.1109/tvt.2016.2522468","title":"Analyzing Dependent Placements of Small Cells in a Two-Layer Heterogeneous Network With a Rate Coverage Constraint","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Upper and lower bounds; Interference (communication); Poisson point process; Telecommunications link; Computer science; Constraint (computer-aided design); Point process; Cellular network; Grid; Residual; Stochastic geometry; Heterogeneous network; Hexagonal crystal system; Mathematical optimization; Topology (electrical circuits); Mathematics; Computer network; Algorithm; Wireless network; Statistics; Telecommunications; Wireless; Combinatorics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001081785,0.00109068,0.0007328616,0.0006336624,0.0004115314,0.0009467378,0.001188364,0.0008825387,0.001158986],"category_scores_gemma":[0.005366586,0.0005715635,0.0005971566,0.0008116146,0.001163462,0.001000938,0.001066522,0.0006510566,0.0001522377],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001919128,"about_ca_system_score_gemma":0.0006251477,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01327729,"about_ca_topic_score_gemma":0.005784463,"domain_scores_codex":[0.9993361,0.0002088603,0.00001902321,0.0001015856,0.0001129056,0.0002214841],"domain_scores_gemma":[0.9964929,0.002135858,0.00061869,0.0001678817,0.0003452434,0.0002394687],"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":[0.00004193048,0.00001460426,0.001122857,0.00001530227,0.00001279897,0.0001202901,0.00001330595,0.9939207,0.001264452,0.002367944,0.0001214449,0.0009843961],"study_design_scores_gemma":[0.000003727968,0.0000175774,0.0003873477,0.000001718311,0.000005983377,0.00001633026,0.00001714136,0.9985554,0.0003010138,0.0006491659,0.00004081949,0.000003828068],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5734653,0.000706193,0.4181309,0.0003982522,0.00004889433,0.00009327381,0.0003196388,0.0001712678,0.006666335],"genre_scores_gemma":[0.9878663,0.0002596691,0.01028147,0.00004493159,0.00001986933,0.000037195,0.0000974623,0.00002298331,0.001370058],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01327729,"threshold_uncertainty_score":0.02640003,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007499709003292688,"score_gpt":0.2098622021501493,"score_spread":0.2023624931468566,"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."}}