{"id":"W4387068346","doi":"10.1109/tmc.2023.3319545","title":"DQ-Based Random Access NOMA for Massive Critical IoT Scenarios in 5G Networks","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ericsson (Canada); Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Computer network; Noma; Aloha; Base station; Low latency (capital markets); Benchmark (surveying); Distributed computing; Node (physics); Key (lock); Transmission (telecommunications); Throughput; Wireless; Telecommunications link; Telecommunications; Computer security","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":[],"consensus_categories":[],"category_scores_codex":[0.0001843286,0.0001995262,0.0002747675,0.0003853485,0.0002330418,0.00006219753,0.0004839831,0.0001678151,0.00001327952],"category_scores_gemma":[0.00004042422,0.0002282793,0.0001131785,0.0008452369,0.00009087369,0.0001076973,0.00000543706,0.0005199302,0.00001562608],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001422609,"about_ca_system_score_gemma":0.00002060214,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007085109,"about_ca_topic_score_gemma":0.00003380847,"domain_scores_codex":[0.9987908,0.000042389,0.0003673496,0.0002585122,0.0001102684,0.0004306673],"domain_scores_gemma":[0.9973998,0.002003555,0.00003508881,0.0004601562,0.00005191339,0.00004948409],"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":[0.00003203982,0.00004220879,0.00001929951,0.00005060368,0.00001265002,0.000003811282,0.00005272649,0.9195592,0.0003344957,0.00004286083,0.00005051196,0.07979958],"study_design_scores_gemma":[0.001290136,0.00004850296,0.00008434849,0.0001486778,0.000008114183,0.000001081973,0.000154272,0.9835163,0.01421069,0.0001505388,0.000163304,0.0002239971],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05284739,0.0001041489,0.9433571,0.0002073548,0.0005746438,0.0006704278,0.00001275847,0.002188144,0.00003800369],"genre_scores_gemma":[0.9926283,0.00005872299,0.006575773,0.00004356505,0.00003512493,0.0005803088,0.000007752886,0.00006178353,0.000008677246],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9397809,"threshold_uncertainty_score":0.9308958,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02614667819666722,"score_gpt":0.3121826186513627,"score_spread":0.2860359404546955,"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."}}