{"id":"W2518975783","doi":"10.1109/wcnc.2016.7564949","title":"Gram-Schmidt precoding for two-tier cellular networks with massive MIMO","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Precoding; MIMO; Transmitter; Telecommunications link; Computer science; Zero-forcing precoding; Algorithm; Base station; User equipment; Multi-user MIMO; Topology (electrical circuits); Channel (broadcasting); Mathematics; Computer network","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":[0.00006339909,0.0001559501,0.0001533634,0.00004730177,0.00004727468,0.00002123345,0.00008205565,0.00006582488,0.00007861311],"category_scores_gemma":[0.00001294276,0.0001004072,0.00003675244,0.00009626841,0.00001709565,0.0002468307,0.00001209889,0.00004560187,0.00002004115],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007965138,"about_ca_system_score_gemma":0.000006555987,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002609687,"about_ca_topic_score_gemma":0.00001318743,"domain_scores_codex":[0.9992831,0.000008714084,0.0001731358,0.0001813786,0.00006152315,0.0002921626],"domain_scores_gemma":[0.9995481,0.00007997857,0.00003883527,0.0002014088,0.00006537121,0.00006635678],"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":[0.00001653876,0.000004449933,0.000182811,0.00003471275,0.00003910329,0.000001622789,0.00003975929,0.9841478,0.007839812,0.001366027,0.0009498217,0.005377558],"study_design_scores_gemma":[0.001918646,0.00008730691,0.00001343311,0.0002135129,0.0000349653,0.000003483065,0.0000641597,0.9449323,0.04546577,0.0002456679,0.006529759,0.0004909601],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001036746,0.0001627431,0.9925889,0.00004343711,0.0003126499,0.000505023,0.000003882259,0.0004217894,0.004924865],"genre_scores_gemma":[0.8958637,0.00001710664,0.09947693,0.00001690856,0.0002591741,0.0001610539,0.00001019067,0.00008243236,0.004112505],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8948269,"threshold_uncertainty_score":0.4094489,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007056636622371813,"score_gpt":0.1991322192069662,"score_spread":0.1920755825845944,"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."}}