{"id":"W2290855852","doi":"10.1109/glocom.2015.7417308","title":"Cognitive MU-MIMO Scheduling in Circular Array Based Heterogeneous Networks","year":2015,"lang":"en","type":"article","venue":"2015 IEEE Global Communications Conference (GLOBECOM)","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Communications Research Centre Canada","funders":"","keywords":"Codebook; Computer science; MIMO; Scheduling (production processes); Spectral efficiency; Wireless; Telecommunications link; Cognitive radio; Computer network; Schedule; Heterogeneous network; Wireless network; Distributed computing; Telecommunications; Algorithm; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000926372,0.0004613762,0.0006623814,0.0003657092,0.000791395,0.0006780079,0.000882656,0.0003872785,0.0007762656],"category_scores_gemma":[0.001877756,0.0002511497,0.0002437122,0.0006718077,0.0007029384,0.0005917549,0.000796361,0.0004112074,0.0001519724],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009029304,"about_ca_system_score_gemma":0.00103581,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004001006,"about_ca_topic_score_gemma":0.004654504,"domain_scores_codex":[0.9993074,0.0001984424,0.00002216323,0.0001271561,0.0001495343,0.0001953515],"domain_scores_gemma":[0.99861,0.0006385671,0.0002152156,0.0001216216,0.000257015,0.0001575222],"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":[0.0006523887,0.000123622,0.001332402,0.00008249282,0.00006847209,0.0003619817,0.0001715174,0.8976316,0.01584884,0.02487832,0.002153035,0.05669532],"study_design_scores_gemma":[0.00001144928,0.00007645421,0.0001823794,0.000002156676,0.000008639523,0.00004905151,0.00002779939,0.9948629,0.001406705,0.003029646,0.0003335134,0.0000093917],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1188915,0.0006449533,0.8744435,0.0002596691,0.000158621,0.00005456805,0.00007232981,0.0002329504,0.005241926],"genre_scores_gemma":[0.9738449,0.0001656171,0.02484935,0.00008884148,0.00004850469,0.00003222745,0.00002659904,0.000009285488,0.0009346422],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004001006,"threshold_uncertainty_score":0.007955432,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05973339818328947,"score_gpt":0.3023882915081686,"score_spread":0.2426548933248791,"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."}}