{"id":"W2123022379","doi":"10.1109/vtcf.2006.82","title":"Adaptive Space-Time Trellis Codes Based on Convolutional Codes","year":2006,"lang":"en","type":"article","venue":"IEEE Vehicular Technology Conference","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Convolutional code; Space–time trellis code; Computer science; Fountain code; Turbo code; Block code; Algorithm; Linear code; Phase-shift keying; Trellis (graph); Puncturing; Encoder; Bit error rate; Code (set theory); Concatenated error correction code; Raptor code; Serial concatenated convolutional codes; Code rate; Trellis modulation; Generator matrix; Decoding methods; Telecommunications; Fading","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":[0.0005166474,0.0004860485,0.0003524515,0.0007034218,0.0002670845,0.0005549119,0.0007282426,0.0006048027,0.001537509],"category_scores_gemma":[0.002538412,0.0001868004,0.0003265482,0.001046934,0.000580342,0.000732457,0.0004045918,0.0008086124,0.0006952089],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009612019,"about_ca_system_score_gemma":0.0009511699,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006945239,"about_ca_topic_score_gemma":0.008456283,"domain_scores_codex":[0.9993141,0.0001380189,0.00003702165,0.00007585939,0.0003416247,0.00009331149],"domain_scores_gemma":[0.9982976,0.0006677462,0.0002019191,0.0002354542,0.0005403559,0.00005686424],"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":[0.0007047858,0.0001041209,0.00172852,0.0003165487,0.0001352956,0.0004840384,0.0001862792,0.5035622,0.1158123,0.1072309,0.006360348,0.2633747],"study_design_scores_gemma":[0.00006681063,0.0002179652,0.00101305,0.00004132355,0.0000398015,0.0003715066,0.00001202535,0.922922,0.0523601,0.01125006,0.01163811,0.00006710801],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0758118,0.001921539,0.9073926,0.0002624032,0.0002261421,0.0001337705,0.0003284432,0.001701173,0.01222211],"genre_scores_gemma":[0.728099,0.001722838,0.2578901,0.0001766589,0.0001564196,0.0001610735,0.0005349461,0.0001055705,0.0111535],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006945239,"threshold_uncertainty_score":0.01380968,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009948198548394276,"score_gpt":0.2140095797717071,"score_spread":0.2040613812233129,"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."}}