{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","open_science"],"consensus_categories":[],"category_scores_codex":[0.0006687686,0.0004696846,0.0005184656,0.0002367134,0.001817053,0.0001516224,0.006354258,0.0001770451,0.0002998559],"category_scores_gemma":[0.0002218664,0.0006140553,0.0002301531,0.001462344,0.000391366,0.0002535367,0.002137557,0.00132772,0.0000407381],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001272272,"about_ca_system_score_gemma":0.0002692683,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007414095,"about_ca_topic_score_gemma":0.0001904658,"domain_scores_codex":[0.9970694,0.0003790642,0.0008247725,0.0005474444,0.0004621504,0.0007171413],"domain_scores_gemma":[0.9932697,0.0005676157,0.0002370642,0.005559974,0.0002123215,0.0001533678],"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":[0.00007192738,0.0002414774,0.0003688577,0.00008201982,0.0001153283,0.000002135441,0.0001818889,0.9300164,0.001137989,0.02711629,0.01413582,0.02652987],"study_design_scores_gemma":[0.001056435,0.0001106148,0.00006768319,0.00003893603,0.00003587094,0.00002343434,0.001334401,0.8260646,0.0004474981,0.003261618,0.1669394,0.0006194462],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02796408,0.01481204,0.9217385,0.01188344,0.001223926,0.003398539,0.003006748,0.006268366,0.009704364],"genre_scores_gemma":[0.9115546,0.0007816731,0.08342575,0.0004785149,0.00003092802,0.002470929,0.00104504,0.00007863358,0.0001339855],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8835905,"threshold_uncertainty_score":0.9996311,"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."}}