{"id":"W4211037983","doi":"10.32920/ryerson.14657925","title":"Power allocation in OFDM-based cognitive radio systems","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Subcarrier; Underlay; Orthogonal frequency-division multiplexing; Computer science; Transmitter power output; Cognitive radio; Mathematical optimization; Transmission (telecommunications); Algorithm; Interference (communication); Transmitter; Channel (broadcasting); Telecommunications; Wireless; Signal-to-noise ratio (imaging); 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003353303,0.000306949,0.0004424831,0.0001437307,0.000277635,0.0006929976,0.0004033166,0.0004121546,0.0007942228],"category_scores_gemma":[0.001237517,0.0001426161,0.0001847957,0.0003163253,0.0005161232,0.0004473905,0.0004033927,0.0003282596,0.0001889806],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005208781,"about_ca_system_score_gemma":0.0007074164,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002529615,"about_ca_topic_score_gemma":0.001620932,"domain_scores_codex":[0.9997044,0.00009440615,0.00001222209,0.00005998978,0.00007895816,0.00004987976],"domain_scores_gemma":[0.9997967,0.0001128849,0.00002820583,0.00001846195,0.00003660794,0.000007196358],"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":[0.00004936663,0.00002400245,0.000492935,0.00005935718,0.00002024042,0.0001000868,0.00005910695,0.8920842,0.003516085,0.03528267,0.000868646,0.0674433],"study_design_scores_gemma":[0.00000454927,0.00001562079,0.0000780927,0.000003093,0.000003147766,0.00002389298,0.000008265622,0.9925795,0.0005501293,0.006136117,0.0005948106,0.00000279123],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02002031,0.0004090354,0.9756428,0.0001069068,0.00003663625,0.0000174702,0.00001814456,0.00007339478,0.003675348],"genre_scores_gemma":[0.8527625,0.0006001841,0.1429463,0.00009409398,0.00004876608,0.00007074693,0.00003182631,0.00002286181,0.003422697],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002529615,"threshold_uncertainty_score":0.005029798,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008500526822760798,"score_gpt":0.2205385520084981,"score_spread":0.2120380251857373,"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."}}