{"id":"W4406090099","doi":"10.1109/ojcoms.2025.3526759","title":"Active RIS-NOMA Uplink in URLLC, Jamming Mitigation via Surrogate and Deep Learning","year":2025,"lang":"en","type":"article","venue":"IEEE Open Journal of the Communications Society","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Ministère de la Défense Nationale","keywords":"Noma; Telecommunications link; Jamming; Computer science; Computer network; Physics","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":[0.0004115804,0.00007858953,0.0001516088,0.00004171863,0.0002652648,0.00008942203,0.0009982545,0.00006010731,0.000001890694],"category_scores_gemma":[0.00004107811,0.00006740032,0.00008149195,0.0002496523,0.00008857888,0.0002479419,0.0003403233,0.0006184158,5.640812e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001134334,"about_ca_system_score_gemma":0.0000290133,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004004196,"about_ca_topic_score_gemma":0.00004669978,"domain_scores_codex":[0.9993821,0.0001452248,0.0002605679,0.00005511244,0.00006348243,0.00009353853],"domain_scores_gemma":[0.9990742,0.000205992,0.0001427862,0.0004451758,0.0001105897,0.00002129165],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009153989,0.0003243185,0.03046717,0.0001075943,0.00148487,0.000006759859,0.03581798,0.1192031,0.1810976,0.001997198,0.01126864,0.6181332],"study_design_scores_gemma":[0.002266295,0.00009577972,0.05479386,0.002580975,0.000256658,0.0001095159,0.007223708,0.7724566,0.1216036,0.02116781,0.01685843,0.000586825],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9539596,0.004001223,0.0306431,0.004453612,0.0003696953,0.0004627918,0.000001388371,0.00008628398,0.006022329],"genre_scores_gemma":[0.9812177,0.0017662,0.01684698,0.00008417788,0.00001272578,0.000005448134,7.963046e-7,0.00001001264,0.0000559029],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6532535,"threshold_uncertainty_score":0.2748505,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01676295254077728,"score_gpt":0.2831495680726141,"score_spread":0.2663866155318368,"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."}}