{"id":"W4315629608","doi":"10.1109/globecom48099.2022.10000921","title":"Uplink Cluster-Based Radio Resource Scheduling for HetNet mMTC Scenarios","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Qatar University","keywords":"Telecommunications link; Computer science; Heterogeneous network; Scheduling (production processes); Noma; Spectral efficiency; Computer network; User equipment; Orthogonality; Radio access technology; Orthogonal frequency-division multiplexing; Radio resource management; Single antenna interference cancellation; Proportionally fair; Cellular network; Distributed computing; Base station; Dynamic priority scheduling; Channel (broadcasting); Wireless; Telecommunications; Round-robin scheduling; Wireless network; Engineering; Quality of service","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":[0.0005810523,0.0003588859,0.0004214174,0.0003400579,0.0005685544,0.0006927903,0.0006017184,0.0006679469,0.002140642],"category_scores_gemma":[0.001064712,0.0001902487,0.0003060779,0.0004946376,0.0005433168,0.0004709889,0.0005641838,0.0004353381,0.0001696159],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0013022,"about_ca_system_score_gemma":0.001355171,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01528157,"about_ca_topic_score_gemma":0.01514936,"domain_scores_codex":[0.9997213,0.0001134007,0.000005918101,0.00002828731,0.00004349348,0.00008746724],"domain_scores_gemma":[0.999511,0.0002196839,0.00006581524,0.00003200862,0.0001115779,0.00005996479],"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":[0.0000250916,0.00001286162,0.0002685932,0.000006453657,0.000004253083,0.00002662282,0.00001144184,0.9956306,0.0002734697,0.002721124,0.0002462067,0.0007733052],"study_design_scores_gemma":[0.000005150008,0.00001366195,0.0001023303,0.000001049282,0.000001999853,0.000005646848,0.00001282718,0.9989941,0.00009655369,0.0006329305,0.0001317243,0.000002036937],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6495785,0.0005768565,0.2934015,0.0007274395,0.0001643962,0.0002254396,0.0005297358,0.0004324942,0.05436359],"genre_scores_gemma":[0.9900023,0.00006379925,0.008532061,0.00004220292,0.000009731224,0.0000365479,0.00005166659,0.00001734523,0.001244379],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01528157,"threshold_uncertainty_score":0.0303852,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03619298857933601,"score_gpt":0.2820206379541715,"score_spread":0.2458276493748355,"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."}}