{"id":"W2151970778","doi":"10.1109/twc.2009.12.090394","title":"Energy-efficient power allocation in OFDM-based cognitive radio systems: A risk-return model","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":148,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba; University of British Columbia","funders":"","keywords":"Subcarrier; Cognitive radio; Computer science; Orthogonal frequency-division multiplexing; Resource allocation; Mathematical optimization; Interference (communication); Optimization problem; Transmission (telecommunications); Wireless; Computer network; Telecommunications; Algorithm; Channel (broadcasting); Mathematics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002058948,0.0002826097,0.0002888818,0.0004911118,0.0002945966,0.00005172347,0.0004609381,0.0001806454,0.000006573178],"category_scores_gemma":[0.000008234391,0.0003304569,0.0001004439,0.0007660749,0.00007943248,0.0002063779,0.000001851844,0.0004945952,0.00001698819],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004531437,"about_ca_system_score_gemma":0.00007543898,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009612876,"about_ca_topic_score_gemma":0.0002683623,"domain_scores_codex":[0.9984306,0.0002147147,0.0005758387,0.0002733979,0.0002054073,0.0003001156],"domain_scores_gemma":[0.9981056,0.0002753042,0.0001317162,0.001201448,0.0001837701,0.0001020867],"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":[0.00002254007,0.0003687335,0.000005251847,0.00001401235,0.00002798223,6.080218e-7,0.000618342,0.9919841,0.001290053,0.0005986652,0.00001727291,0.005052474],"study_design_scores_gemma":[0.000814173,0.00004846604,0.00003235473,0.0002747633,0.00004481136,0.000003246983,0.0002774063,0.9950413,0.003071564,0.0000450336,0.00003413045,0.0003128159],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01078576,0.0005172223,0.9861049,0.0001461926,0.000201588,0.0005465253,0.0001327465,0.0005479829,0.001017102],"genre_scores_gemma":[0.9944448,0.0004933078,0.004384755,0.00005641682,0.000009557276,0.0004267957,0.00006014476,0.00005967914,0.00006447633],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9836591,"threshold_uncertainty_score":0.9999148,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01472270564654043,"score_gpt":0.2402256647842329,"score_spread":0.2255029591376925,"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."}}