{"id":"W2757653856","doi":"10.1109/twc.2017.2753771","title":"Coverage Analysis of Multi-Stream MIMO HetNets With MRC Receivers","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"MIMO; Computer science; Maximal-ratio combining; Spectral efficiency; Data stream; Data stream mining; Multiplexing; Stochastic geometry; Coverage probability; Leverage (statistics); Interference (communication); Channel (broadcasting); Algorithm; Fading; Telecommunications; Data mining; Statistics; Mathematics","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":[0.001045463,0.0008829638,0.0006517827,0.0006890735,0.0003264103,0.0008707005,0.0007031096,0.0007691347,0.001386801],"category_scores_gemma":[0.003128301,0.0003630823,0.0005539572,0.0006686536,0.0009355449,0.0006937162,0.00120113,0.000438305,0.0002603028],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001331747,"about_ca_system_score_gemma":0.0004698949,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004153298,"about_ca_topic_score_gemma":0.001829004,"domain_scores_codex":[0.9994364,0.0002154687,0.00001514559,0.00005731725,0.0001538076,0.0001218524],"domain_scores_gemma":[0.9973908,0.001735133,0.0003161097,0.0001064871,0.0003633899,0.00008814827],"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":[0.00007097342,0.00001189929,0.001469226,0.00004301608,0.00002736251,0.0001939953,0.00005027953,0.9838634,0.003036253,0.008205345,0.0003311003,0.002697144],"study_design_scores_gemma":[0.000002669211,0.00002297308,0.0005896927,0.000007304796,0.00001000416,0.00006263173,0.0000277279,0.996899,0.0005601223,0.001693388,0.0001192819,0.000005152463],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.393929,0.002630751,0.5707052,0.0006402548,0.000059209,0.00006672618,0.0005270169,0.0003523822,0.03108946],"genre_scores_gemma":[0.9925557,0.0006104227,0.005197032,0.00006484146,0.00003525328,0.0000281458,0.0000905935,0.00002252278,0.001395616],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004153298,"threshold_uncertainty_score":0.009662569,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02655344108157135,"score_gpt":0.272023486168136,"score_spread":0.2454700450865647,"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."}}