{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001479366,0.0002814926,0.000287917,0.0002453263,0.0008043164,0.00009537913,0.00126333,0.0001827178,0.0000119239],"category_scores_gemma":[0.0000458903,0.0003333726,0.0001149537,0.0005761821,0.0002951249,0.0004716521,0.00002531874,0.0005067884,0.00003135789],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001049538,"about_ca_system_score_gemma":0.00002321449,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001068645,"about_ca_topic_score_gemma":0.00009876424,"domain_scores_codex":[0.9985762,0.00006953433,0.000606046,0.0003116661,0.0001751292,0.0002614171],"domain_scores_gemma":[0.9969355,0.00034894,0.0001329709,0.002239355,0.0002121922,0.0001310585],"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":[0.00005812332,0.0002494216,0.000005217989,0.0002538805,0.0002118512,4.08428e-7,0.001721345,0.5718941,0.08892419,0.004104352,0.0006400043,0.3319371],"study_design_scores_gemma":[0.0006710291,0.0000754428,0.00009527293,0.00004163534,0.00004733542,0.000005870737,0.0003769681,0.8072472,0.1875512,0.002228009,0.001317779,0.0003422869],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008064859,0.0004175556,0.9836684,0.004833377,0.0001328396,0.0008189685,0.0001934143,0.001805749,0.00006484456],"genre_scores_gemma":[0.97443,0.001809373,0.02216452,0.0001437663,0.00001905119,0.001276554,0.00004016848,0.000085198,0.00003142602],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9663651,"threshold_uncertainty_score":0.9999118,"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."}}