{"id":"W3042527581","doi":"10.1109/twc.2020.3007545","title":"Joint User Identification, Channel Estimation, and Signal Detection for Grant-Free NOMA","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":94,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Science and Technology Commission of Shanghai Municipality; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Channel state information; Noma; Channel (broadcasting); Overhead (engineering); Multiuser detection; Joint (building); Algorithm; Computer engineering; Wireless; Telecommunications; Telecommunications link; Code division multiple access; Engineering","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":[0.001310905,0.0007433256,0.0008397591,0.0004726464,0.0007941062,0.0007445868,0.001068682,0.000715766,0.001201572],"category_scores_gemma":[0.004132755,0.0004411642,0.00050955,0.000649902,0.0009775213,0.001437659,0.001840664,0.001444621,0.0007573471],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006118098,"about_ca_system_score_gemma":0.002001673,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002652525,"about_ca_topic_score_gemma":0.003449461,"domain_scores_codex":[0.9988393,0.0004544176,0.00003782736,0.0002160052,0.0003184729,0.0001338895],"domain_scores_gemma":[0.9986919,0.0006192848,0.0001686171,0.0003088168,0.0001366407,0.00007480354],"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":[0.0004384509,0.0002224731,0.002692482,0.0001586886,0.0001120405,0.0001688541,0.0002651658,0.4454323,0.01791903,0.08593966,0.003358583,0.4432922],"study_design_scores_gemma":[0.00001394214,0.00005348962,0.0002710142,0.000004330951,0.000008553639,0.00006212312,0.00001435723,0.9876218,0.002519643,0.008281413,0.001135081,0.00001431151],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008754289,0.0001277024,0.9899374,0.0001007066,0.00002714097,0.00003152397,0.00001637152,0.0003762829,0.0006285266],"genre_scores_gemma":[0.5702617,0.0001853461,0.4260769,0.0001414259,0.00007097493,0.0001609759,0.0001343965,0.00005841424,0.002909812],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002652525,"threshold_uncertainty_score":0.006932795,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03867247358460511,"score_gpt":0.2481908137102704,"score_spread":0.2095183401256652,"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."}}