{"id":"W4413925784","doi":"10.1109/tmc.2025.3604722","title":"Uplink Resource Allocation for RSMA-Aided Digital Twin-Assisted User-Centric Cell-Free Massive MIMO Systems","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; University of Calgary; École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Telecommunications link; Resource allocation; MIMO; Computer network; Distributed computing","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":[0.0001540152,0.0003274103,0.0003639011,0.0004039653,0.0003320982,0.000193859,0.0003192927,0.0001958867,0.000005026533],"category_scores_gemma":[0.00002225192,0.0003806389,0.0001711766,0.0007612426,0.00002852387,0.0002693262,0.000004905693,0.0002713011,0.00002372508],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004630229,"about_ca_system_score_gemma":0.00004674679,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001089708,"about_ca_topic_score_gemma":0.000004467361,"domain_scores_codex":[0.9982431,0.00004373287,0.0006790579,0.0004438913,0.0001633088,0.0004269239],"domain_scores_gemma":[0.9985402,0.0004631353,0.0001221673,0.0005919744,0.0001913332,0.00009116781],"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":[0.00002503229,0.00008979683,0.000007724148,0.000408316,0.00007667753,0.000001514763,0.0001311655,0.9820622,0.001729907,0.00004609604,0.001236059,0.01418547],"study_design_scores_gemma":[0.001520696,0.00007609966,0.00001027891,0.000349867,0.00008057791,0.000007204402,0.000484962,0.9828368,0.01072587,0.00001262474,0.003549864,0.0003451247],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009138657,0.000317925,0.984515,0.00002824778,0.001918163,0.001889638,0.0000681824,0.0009635237,0.001160623],"genre_scores_gemma":[0.9902992,0.00001159242,0.00728533,0.000021814,0.000116851,0.0003656433,0.00005117703,0.00009165941,0.001756728],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9811605,"threshold_uncertainty_score":0.9998646,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008187402815690583,"score_gpt":0.2244636609554674,"score_spread":0.2162762581397768,"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."}}