{"id":"W1996911166","doi":"10.1002/ett.2577","title":"Location‐assisted clustering and scheduling for coordinated homogeneous and heterogeneous cellular networks","year":2012,"lang":"en","type":"article","venue":"Transactions on Emerging Telecommunications Technologies","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Base station; Heterogeneous network; Computer science; MIMO; Channel state information; Cellular network; Computer network; Telecommunications link; Scheduling (production processes); Homogeneous; Precoding; Transmission (telecommunications); Transmitter; Spectral efficiency; Real-time computing; Wireless network; Wireless; Channel (broadcasting); Telecommunications; Mathematical optimization; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001474333,0.0002162833,0.0002036489,0.0003043937,0.0005513584,0.00004387837,0.000228568,0.0001956756,0.000002908707],"category_scores_gemma":[0.00003579673,0.0002480133,0.00003687331,0.0004681495,0.0001192761,0.0002412989,0.00002205903,0.0002689702,0.000001845055],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000878291,"about_ca_system_score_gemma":0.000006146417,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006465732,"about_ca_topic_score_gemma":0.000024021,"domain_scores_codex":[0.9990886,0.00002762895,0.0003107462,0.0001827151,0.00004811469,0.0003422099],"domain_scores_gemma":[0.9989954,0.0001700003,0.00006416713,0.0006385625,0.00008291216,0.00004898416],"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":[0.000005280726,0.00003494866,0.00003408366,0.00007322779,0.00006100344,1.297772e-7,0.0001328652,0.9180868,0.001600216,0.00009487266,0.00000448181,0.07987206],"study_design_scores_gemma":[0.0003062055,0.00003731829,0.00002938803,0.00009586865,0.0000637876,0.00004444252,0.0007681227,0.9888249,0.008803466,0.00008107647,0.0006338861,0.0003115204],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00957516,0.01408349,0.9729707,0.0002166306,0.0001368607,0.000507741,0.000007228526,0.00245425,0.00004789088],"genre_scores_gemma":[0.8475198,0.002856509,0.1491287,0.000007116352,0.00001099832,0.0003835709,0.00001781512,0.00005540318,0.00002013299],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8379446,"threshold_uncertainty_score":0.9999972,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01588804566416313,"score_gpt":0.2407474225886459,"score_spread":0.2248593769244828,"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."}}