{"id":"W2966955883","doi":"10.1109/tcomm.2019.2935728","title":"Energy and Spectral Efficiency Analysis for a Device-to-Device-Enabled Millimeter-Wave OFDMA Cellular Network","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Millimeter-Wave Propagation and Modeling","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Spectral efficiency; Cellular network; Stochastic geometry; Base station; Computer science; Path loss; Interference (communication); Electronic engineering; Efficient energy use; Bandwidth (computing); Transmitter power output; Coverage probability; Topology (electrical circuits); Computer network; Engineering; Mathematics; Electrical engineering; Telecommunications; Beamforming; Wireless; Statistics; Transmitter","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":[0.0008061281,0.0005482605,0.0005109736,0.0006861793,0.0003337233,0.0007941505,0.0005502722,0.0004594279,0.0008671373],"category_scores_gemma":[0.002362727,0.0001849002,0.0004386443,0.0008090613,0.0005097936,0.0007842328,0.0006362494,0.0003862127,0.000163858],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001176547,"about_ca_system_score_gemma":0.0005054202,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002097606,"about_ca_topic_score_gemma":0.001680436,"domain_scores_codex":[0.9995735,0.0001376173,0.00001755795,0.00004733156,0.0001438602,0.00008020698],"domain_scores_gemma":[0.9989579,0.000698304,0.00008424334,0.00006316313,0.0001769831,0.00001947428],"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":[0.00006074919,0.00002780603,0.001144015,0.0000444928,0.00002601008,0.00008981136,0.00003908678,0.9608258,0.006470252,0.01739606,0.0003140628,0.01356191],"study_design_scores_gemma":[0.000001027322,0.00002445816,0.0003823672,0.000003045995,0.000005982178,0.00003242291,0.00001269679,0.9970062,0.001091645,0.001287011,0.00014975,0.000003335018],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.177833,0.001043421,0.8123898,0.0003121888,0.0000293678,0.00004679982,0.00009673677,0.0001701573,0.008078511],"genre_scores_gemma":[0.9806183,0.000551141,0.01748809,0.00004855152,0.00001700275,0.00003511971,0.00004969241,0.0000188267,0.001173393],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002097606,"threshold_uncertainty_score":0.008536518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03424849258893371,"score_gpt":0.2414461284680043,"score_spread":0.2071976358790706,"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."}}