{"id":"W2791224295","doi":"10.1109/tvt.2018.2809616","title":"Content-Aware Cooperative Transmission in HetNets With Consideration of Base Station Height","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Macrocell; Base station; Computer science; Stochastic geometry; Cache; Cellular network; Transmission (telecommunications); Computer network; Interference (communication); Heterogeneous network; Spectral efficiency; Wireless network; Wireless; Telecommunications; Mathematics; 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":[],"consensus_categories":[],"category_scores_codex":[0.0000646302,0.000174165,0.0002395222,0.0005006552,0.00007799918,0.000007462843,0.00006829834,0.0002305788,0.00004515457],"category_scores_gemma":[0.000004379115,0.0001605141,0.00002780482,0.0006159576,0.0001655418,0.0002034007,4.068251e-7,0.0002307257,0.00001077396],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001032247,"about_ca_system_score_gemma":0.00003252746,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002263994,"about_ca_topic_score_gemma":0.0003987978,"domain_scores_codex":[0.9991156,0.00004099235,0.0003315257,0.0002234932,0.0001094911,0.000178858],"domain_scores_gemma":[0.9994256,0.00003373025,0.00005473122,0.0002179471,0.0002342471,0.00003372303],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00008500507,0.00009269588,0.00004057186,0.00004432504,0.00004989167,0.00001366816,0.0003625374,0.8631871,0.1275859,0.0002117617,0.00001196856,0.008314574],"study_design_scores_gemma":[0.001126288,0.0004187943,0.00002296202,0.000172871,0.00002114712,0.00002707553,0.0003938851,0.2471587,0.7503516,0.00007607137,0.00006459545,0.0001660437],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1478155,0.00007107305,0.8510745,0.0001316112,0.0001028012,0.0004369721,0.00001846386,0.0002909953,0.00005815181],"genre_scores_gemma":[0.9907291,0.00005346458,0.009019715,0.00001817905,0.000008356462,0.00009740435,0.00001214219,0.00003551274,0.00002611753],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8429136,"threshold_uncertainty_score":0.6545574,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01299389399634954,"score_gpt":0.2206890569744755,"score_spread":0.207695162978126,"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."}}