{"id":"W2189288201","doi":"10.1109/pimrc.2015.7343466","title":"Energy-efficient subcarrier power allocation for cognitive radio networks using statistical interference model","year":2015,"lang":"en","type":"article","venue":"","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Subcarrier; Cognitive radio; Transmitter; Computer science; Interference (communication); Channel (broadcasting); Transmitter power output; Mathematical optimization; Orthogonal frequency-division multiplexing; Electronic engineering; Transmission (telecommunications); Telecommunications; Computer network; Wireless; Mathematics; 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":[0.0003802755,0.0002022761,0.0002234144,0.00009094709,0.0001393381,0.0002285035,0.000262835,0.00008426861,0.000008261552],"category_scores_gemma":[0.00009617216,0.0001853154,0.00006343464,0.0002763461,0.00008502649,0.0002261645,0.0001442785,0.0001204463,0.000002154649],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001523143,"about_ca_system_score_gemma":0.0002193869,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003560467,"about_ca_topic_score_gemma":0.00002803836,"domain_scores_codex":[0.9983945,0.00007779385,0.0002801893,0.0005285325,0.0002500479,0.0004689644],"domain_scores_gemma":[0.9985869,0.0003113512,0.00008427932,0.0002311192,0.0005154588,0.0002709163],"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":[0.0001046227,0.00009950376,0.00003914591,0.000003064763,0.00004283158,0.00001202953,0.0006983479,0.4706084,0.00008791155,0.4959424,0.001270715,0.031091],"study_design_scores_gemma":[0.0005884535,0.0001348082,0.00002949471,0.00003172217,0.00002059807,0.0000263105,0.0001076413,0.9930908,0.0002321208,0.005402131,0.00006961908,0.0002662909],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007834058,0.0001133641,0.9890168,0.000166421,0.0004208028,0.0002107426,0.000008344267,0.0001149656,0.002114554],"genre_scores_gemma":[0.9080267,0.000003204457,0.09127565,0.0004233263,0.0001028865,0.00001052348,0.00001565391,0.00001701659,0.0001250422],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9001926,"threshold_uncertainty_score":0.7556942,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04133013234964315,"score_gpt":0.2794566058188324,"score_spread":0.2381264734691893,"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."}}