{"id":"W4407154987","doi":"10.1109/tits.2025.3531663","title":"An Improved Nonlinear Precoding Scheme in Multicarrier Signaling Optimization for Transportation Networks Applications","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"PAPR reduction in OFDM","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"Fundamental Research Funds for the Central Universities; King Saud University; National Natural Science Foundation of China","keywords":"Precoding; Scheme (mathematics); Nonlinear system; Computer science; Zero-forcing precoding; Electronic engineering; Engineering; Computer network; Mathematics; MIMO; Channel (broadcasting); Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002950626,0.000458371,0.0003141632,0.000188733,0.0002432909,0.0003235154,0.0003644028,0.0004368977,0.001329314],"category_scores_gemma":[0.0007470549,0.0001378623,0.0002873846,0.0003907,0.0003712495,0.0004367603,0.0003324968,0.0006313397,0.0003251149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003764338,"about_ca_system_score_gemma":0.0007744498,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002651052,"about_ca_topic_score_gemma":0.002976222,"domain_scores_codex":[0.9998142,0.00005192902,0.000009135393,0.00003458812,0.00007266168,0.00001738318],"domain_scores_gemma":[0.9998537,0.00005241069,0.00001716483,0.00001607108,0.00005446135,0.000006172123],"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":[0.0001211873,0.00007018065,0.0004168516,0.0001678487,0.00003103343,0.0001063173,0.0001704857,0.7251416,0.04317646,0.04163976,0.002421869,0.1865364],"study_design_scores_gemma":[0.000006191717,0.0000320574,0.00006031217,0.000004560216,0.000003618326,0.00002248937,0.000005492383,0.9954235,0.002181979,0.001410041,0.0008450582,0.000004670144],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01152528,0.0002548728,0.9853185,0.0001407807,0.00004114028,0.00002274666,0.00001757199,0.00007750739,0.002601439],"genre_scores_gemma":[0.551971,0.0008047422,0.4395072,0.000164465,0.00008517867,0.0001241247,0.0000914103,0.0000328691,0.00721892],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002651052,"threshold_uncertainty_score":0.005271196,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01421759414481651,"score_gpt":0.2671015657136103,"score_spread":0.2528839715687938,"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."}}