{"id":"W1566257247","doi":"10.1109/jsac.2015.2435391","title":"HetHetNets: Heterogeneous Traffic Distribution in Heterogeneous Wireless Cellular Networks","year":2015,"lang":"en","type":"preprint","venue":"IEEE Journal on Selected Areas in Communications","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Heterogeneous network; Computer science; Poisson distribution; Base station; Wireless network; Spatial correlation; Cellular network; Moment (physics); Spatial heterogeneity; User equipment; Wireless; Computer network; Statistics; Telecommunications; 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":[0.0005137035,0.0006292886,0.0004384023,0.0004749433,0.0003679433,0.0009096673,0.0007862096,0.0004875396,0.0006323179],"category_scores_gemma":[0.001103553,0.0002156593,0.0003560713,0.001091067,0.0005392231,0.0009991142,0.0006096415,0.0004389253,0.000178544],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007686371,"about_ca_system_score_gemma":0.00042418,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004381858,"about_ca_topic_score_gemma":0.003386897,"domain_scores_codex":[0.9995849,0.0001452226,0.00001244909,0.00009044975,0.0001045185,0.00006263682],"domain_scores_gemma":[0.9996169,0.0001869476,0.00006658838,0.0000460292,0.00006632612,0.00001719359],"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":[0.00005666569,0.00003184091,0.002463365,0.00005570371,0.00003390437,0.000384327,0.00006979172,0.8692625,0.005073475,0.09139284,0.001696138,0.02947946],"study_design_scores_gemma":[0.00000217715,0.00001245375,0.0004162959,0.000003185381,0.000005140539,0.00005840505,0.00001940907,0.988553,0.0005741444,0.009303331,0.001046357,0.000005960116],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06206753,0.0005154188,0.9325767,0.0001759439,0.00007377708,0.00003970725,0.0002471314,0.0002370713,0.004066703],"genre_scores_gemma":[0.9517074,0.001002145,0.0425358,0.00009779674,0.00008963649,0.00007888267,0.0003169633,0.00005573549,0.00411562],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004381858,"threshold_uncertainty_score":0.008712709,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0258876139237047,"score_gpt":0.2655454055180485,"score_spread":0.2396577915943438,"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."}}