{"id":"W2165146356","doi":"10.1109/vetecs.2011.5956753","title":"Channel Prediction-Based Adaptive Power Control for Dynamic Wireless Communications","year":2011,"lang":"en","type":"article","venue":"","topic":"Wireless Communication Networks Research","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Channel state information; Orthogonal frequency-division multiplexing; Transmitter power output; Computer science; Subcarrier; Power control; Base station; Channel (broadcasting); Telecommunications link; Electronic engineering; Path loss; Wireless; Power (physics); Telecommunications; Engineering; Transmitter","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":[0.0005591397,0.000148901,0.000183347,0.0001658489,0.0003995416,0.00008012327,0.00417953,0.00009577112,0.0000431295],"category_scores_gemma":[0.00004096619,0.0001397322,0.000106769,0.0004299847,0.0002112513,0.0003917016,0.0004669847,0.0002440817,0.00004840589],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009520871,"about_ca_system_score_gemma":0.0001875059,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000628561,"about_ca_topic_score_gemma":0.0001147937,"domain_scores_codex":[0.9984879,0.0002420554,0.0003086571,0.0003254184,0.0002601312,0.0003758685],"domain_scores_gemma":[0.9952395,0.0007848494,0.0001112882,0.003221474,0.0005000111,0.0001428886],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003381138,0.002141167,0.0008786418,0.00003464452,0.0003532979,0.000003227356,0.004949394,0.004989668,0.0004228108,0.9409871,0.007287871,0.03761404],"study_design_scores_gemma":[0.0009386307,0.0001834132,0.002183823,0.0000174001,0.00000562071,0.000001358178,0.00009144795,0.9929316,0.0001307937,0.002736466,0.0006351094,0.0001443368],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0002737337,0.0001489322,0.9886425,0.002379909,0.0001250401,0.0008547231,0.00003589624,0.0003768808,0.007162414],"genre_scores_gemma":[0.919236,0.00003228307,0.07946024,0.0004544802,0.000009905587,0.0004413944,0.00001837916,0.00001845568,0.0003288499],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9879419,"threshold_uncertainty_score":0.7766677,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0574282096669337,"score_gpt":0.2897329417753722,"score_spread":0.2323047321084385,"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."}}