{"id":"W4293095088","doi":"10.1109/vtc2022-spring54318.2022.9860470","title":"Deep Learning-based List Sphere Decoding for Faster-than-Nyquist (FTN) Signaling Detection","year":2022,"lang":"en","type":"article","venue":"2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring)","topic":"PAPR reduction in OFDM","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Hypersphere; Orthonormal basis; Decoding methods; Algorithm; Nyquist–Shannon sampling theorem; Computer science; Artificial intelligence; Mathematics; Physics; Computer vision","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":[0.001510625,0.0008080824,0.0009177008,0.0006206379,0.0005316553,0.001076692,0.001391987,0.001016822,0.001731323],"category_scores_gemma":[0.005589935,0.0003940261,0.0004328878,0.0008735508,0.0009091811,0.001728572,0.001443935,0.00160097,0.001005192],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001373943,"about_ca_system_score_gemma":0.002496663,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005927643,"about_ca_topic_score_gemma":0.007906577,"domain_scores_codex":[0.9989308,0.000332668,0.0000680599,0.0001490619,0.0003769921,0.0001423658],"domain_scores_gemma":[0.9981127,0.0008806846,0.0001865239,0.0002553117,0.000467522,0.00009731148],"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":[0.0003677839,0.0001451972,0.001479841,0.0001048293,0.00005014751,0.0001086323,0.000201105,0.5552185,0.01550018,0.05482014,0.004945172,0.3670585],"study_design_scores_gemma":[0.000004461384,0.00001406987,0.0000384964,0.000003013201,0.00000147448,0.00001014317,0.000004828072,0.9932024,0.002676662,0.003736783,0.0003028384,0.000004924805],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009689691,0.00009074656,0.9885748,0.0001448277,0.00001983377,0.00001781206,0.00004249471,0.0004790589,0.0009407042],"genre_scores_gemma":[0.3789867,0.0003294776,0.6147575,0.0003185156,0.00006670602,0.0001246549,0.0004768713,0.0001698232,0.00476983],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005927643,"threshold_uncertainty_score":0.01178628,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01189178792381975,"score_gpt":0.2237202123946241,"score_spread":0.2118284244708043,"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."}}