{"id":"W2583380618","doi":"10.1109/glocom.2016.7841638","title":"Boosting the Throughput of HARQ with Off-the-Shelf Codes","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"","keywords":"Hybrid automatic repeat request; Computer science; Network packet; Encoder; Fading; Throughput; Algorithm; Automatic repeat request; Turbo code; Real-time computing; Decoding methods; Computer network; Telecommunications link; Wireless; Telecommunications","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":[0.001766317,0.001033517,0.0008561715,0.0005115519,0.000431077,0.0008341168,0.001345636,0.0007352441,0.001088372],"category_scores_gemma":[0.004724688,0.0002846463,0.0002941587,0.0006320329,0.001138133,0.001462322,0.001191593,0.000889782,0.0003840299],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008500071,"about_ca_system_score_gemma":0.001093652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001061549,"about_ca_topic_score_gemma":0.001544586,"domain_scores_codex":[0.9988141,0.0003994429,0.0000318785,0.0001129542,0.0004527017,0.0001889484],"domain_scores_gemma":[0.997534,0.001421867,0.0001829635,0.0003008086,0.0004772871,0.00008294213],"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":[0.0002412008,0.0001214738,0.0008304444,0.00008623203,0.00005387989,0.0001553179,0.00008166488,0.8827303,0.02565044,0.02911633,0.0009532458,0.05997945],"study_design_scores_gemma":[0.000009741933,0.00007478185,0.00009863121,0.000005199153,0.000008819899,0.00004287972,0.00001077697,0.9918303,0.004582554,0.002978244,0.0003506598,0.000007438363],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06543385,0.0006062362,0.9286568,0.0002050741,0.00007588037,0.00004453967,0.00003288456,0.0004590618,0.004485616],"genre_scores_gemma":[0.9153286,0.0003250017,0.08269793,0.00009380899,0.00006383639,0.00004558928,0.00003475997,0.00007711603,0.001333305],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001766317,"threshold_uncertainty_score":0.0093413,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01104260530421838,"score_gpt":0.2219420529859512,"score_spread":0.2108994476817328,"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."}}