{"id":"W4417131162","doi":"10.1109/tmc.2025.3640955","title":"Leveraging Generative Artificial Intelligence for Uplink Feedback-Free Transmission in 6G FD-RAN","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Telecommunications link; Channel state information; Transmission (telecommunications); Autoencoder; Overhead (engineering); Spectral efficiency; Wireless; Reinforcement learning; Artificial noise","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0006323961,0.0007224062,0.000805231,0.0009712321,0.0008932473,0.0001864965,0.0005566452,0.0004412925,0.0000603733],"category_scores_gemma":[0.00002939627,0.0009037696,0.0003630098,0.001623437,0.00009876165,0.0003614187,0.000007411355,0.001031809,0.00001785106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008932912,"about_ca_system_score_gemma":0.0001823344,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005818297,"about_ca_topic_score_gemma":0.00005451062,"domain_scores_codex":[0.9958011,0.0001729491,0.001800349,0.001073151,0.0002405601,0.0009118894],"domain_scores_gemma":[0.9976637,0.001033811,0.0001975902,0.0007061033,0.0002408411,0.0001579056],"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":[0.00006408722,0.0001817781,0.000002217656,0.0003899743,0.00007760295,0.000003318112,0.002891143,0.6313254,0.005218234,0.0001727097,0.00001564016,0.3596579],"study_design_scores_gemma":[0.0007733293,0.0001686557,0.000003147997,0.001843385,0.00009932108,0.000004548819,0.001777052,0.8653147,0.1276668,0.001504079,0.0002327791,0.0006122047],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006681597,0.001096754,0.9847133,0.0002163993,0.003575288,0.003090765,0.00004072828,0.0003796808,0.0002054451],"genre_scores_gemma":[0.8686854,0.0002224469,0.1301235,0.00005785817,0.0002248844,0.0003265965,0.00001229152,0.0001212617,0.0002257871],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8620037,"threshold_uncertainty_score":0.9993413,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02356467332859442,"score_gpt":0.2812870237724589,"score_spread":0.2577223504438645,"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."}}