{"id":"W2100648790","doi":"10.1109/wcl.2013.052813.130085","title":"Joint Optimization of Bit and Power Loading for Multicarrier Systems","year":2013,"lang":"en","type":"article","venue":"IEEE Wireless Communications Letters","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Communications Research Centre Canada; Memorial University of Newfoundland","funders":"","keywords":"Subcarrier; Transmitter power output; Computer science; Lagrange multiplier; Bit error rate; Mathematical optimization; Power (physics); Bisection method; Throughput; Weighting; Power optimization; Constraint (computer-aided design); Joint (building); Algorithm; Orthogonal frequency-division multiplexing; Wireless; Mathematics; Telecommunications; Decoding methods; Power consumption; Transmitter; Channel (broadcasting); Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009671492,0.0001549129,0.000238391,0.000137993,0.0001264817,0.00005366956,0.0002863673,0.00007655608,0.000007640948],"category_scores_gemma":[0.0000191133,0.0001716648,0.00004304793,0.0002027002,0.0001399276,0.0003456729,0.00004898305,0.0001211682,0.000004019342],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006661104,"about_ca_system_score_gemma":0.000006566009,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002296843,"about_ca_topic_score_gemma":0.000002205199,"domain_scores_codex":[0.999116,0.00004578315,0.0003992592,0.0001410356,0.00009421634,0.0002036737],"domain_scores_gemma":[0.9986812,0.0002021038,0.0001124706,0.0008009519,0.0001404421,0.00006278854],"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":[0.000001682465,0.00001271948,0.00009974286,0.00006618697,0.00003700727,5.505782e-8,0.0002318817,0.9592801,0.03838154,0.0004764689,0.0007183659,0.0006942812],"study_design_scores_gemma":[0.0003013903,0.000009705172,0.0001012367,0.0001011177,0.00001814647,0.000002198584,0.0001280786,0.9970281,0.001872792,0.00001138635,0.0002481848,0.0001776587],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1188486,0.0005352875,0.8787261,0.0004880041,0.000234539,0.0008061645,0.00001677082,0.0001937842,0.0001507581],"genre_scores_gemma":[0.901024,0.0004117525,0.0979709,0.00007421251,0.00002514412,0.0003830084,0.00004277656,0.00005760609,0.00001058303],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7821754,"threshold_uncertainty_score":0.7000288,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01660448581424558,"score_gpt":0.2224114232530679,"score_spread":0.2058069374388223,"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."}}