{"id":"W2972120183","doi":"10.1109/access.2019.2939120","title":"Matching-Based Resource Allocation for Critical MTC in Massive MIMO LTE Networks","year":2019,"lang":"en","type":"article","venue":"IEEE Access","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Quality of service; MIMO; Scheduling (production processes); Computational complexity theory; Latency (audio); Mathematical optimization; Distributed computing; Cellular network; Upper and lower bounds; Computer network; Algorithm; Channel (broadcasting); Mathematics; Telecommunications","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":[0.0008800971,0.0003934205,0.0006237276,0.0002865867,0.0003861516,0.0004849961,0.0006374685,0.0005257875,0.0009672186],"category_scores_gemma":[0.001354493,0.0001897371,0.0002306382,0.0005099903,0.0005251857,0.0005823036,0.0007267337,0.0003734815,0.0001455973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006840485,"about_ca_system_score_gemma":0.0009586726,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001454059,"about_ca_topic_score_gemma":0.001450331,"domain_scores_codex":[0.9996382,0.0001195461,0.00001400878,0.00005026774,0.00008780236,0.00009008095],"domain_scores_gemma":[0.9996177,0.0001896322,0.00007856832,0.00002835319,0.00004587388,0.00003993579],"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":[0.0002082768,0.00009897353,0.0005051753,0.00005339644,0.00002956855,0.00007319577,0.00004871023,0.9130489,0.01119418,0.01572329,0.0009252534,0.05809114],"study_design_scores_gemma":[0.000008985789,0.00004326972,0.00007239146,0.000001747079,0.000004007579,0.00001730016,0.000007775069,0.9954321,0.001321062,0.002918514,0.0001692355,0.000003603533],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05742836,0.0002040783,0.940042,0.0001037889,0.00002871102,0.00004656328,0.00001806277,0.0001392377,0.001989252],"genre_scores_gemma":[0.9417287,0.00009081622,0.05699647,0.00007306974,0.00001689504,0.00003068374,0.00001831148,0.00001412607,0.001031051],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001454059,"threshold_uncertainty_score":0.00496316,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01137215927667498,"score_gpt":0.2767271194417814,"score_spread":0.2653549601651065,"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."}}