{"id":"W2078639479","doi":"10.1109/twc.2014.2350974","title":"Constellation and Rate Selection in Adaptive Modulation and Coding Based on Finite Blocklength Analysis and Its Application to LTE","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Link adaptation; Computer science; MIMO; Constellation; Coding (social sciences); Fading; Orthogonal frequency-division multiplexing; Channel code; Algorithm; Selection (genetic algorithm); Channel capacity; Channel (broadcasting); Telecommunications; Decoding methods; Mathematics; Statistics; Artificial intelligence; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003608597,0.0002239809,0.000277991,0.000981097,0.0003572928,0.00004931951,0.0002064485,0.0001302909,0.000003204098],"category_scores_gemma":[0.00001317902,0.0002731053,0.00003823419,0.001232296,0.00008491444,0.0002375739,0.000009084062,0.0003817905,0.000003390196],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000130524,"about_ca_system_score_gemma":0.00001124844,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004537309,"about_ca_topic_score_gemma":0.0006202605,"domain_scores_codex":[0.9987432,0.0002736224,0.0003761994,0.0003110617,0.0001235423,0.0001723226],"domain_scores_gemma":[0.9980149,0.001011284,0.00008971669,0.000685669,0.0000938141,0.0001046442],"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":[0.00003136512,0.00006595672,0.0002690233,0.00002128432,0.00004609029,3.791774e-8,0.0002287152,0.897095,0.01107373,0.001488163,0.000001326814,0.08967929],"study_design_scores_gemma":[0.0003440052,0.0000779314,0.00378577,0.00007673074,0.0000783091,7.979021e-7,0.00004282928,0.9821783,0.01284577,0.0002137867,0.0001096257,0.000246118],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07812369,0.00005762231,0.9200801,0.0003456816,0.00001606384,0.0006017328,0.00002436618,0.000343854,0.000406841],"genre_scores_gemma":[0.9857746,0.001251307,0.01253422,0.00009749156,0.000006303281,0.0002673396,0.00002351298,0.00003667614,0.000008530266],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9076509,"threshold_uncertainty_score":0.9999721,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01666250855859698,"score_gpt":0.2538660359105194,"score_spread":0.2372035273519225,"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."}}