{"id":"W2463205685","doi":"10.32920/ryerson.14646318","title":"Capacity optimization for radio resource allocation in cognitive networks","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Cognitive radio; Underlay; Computer science; Computer network; Quality of service; Overlay; Radio spectrum; Wireless; Spectrum management; Fading; Radio resource management; Wireless network; Channel (broadcasting); Telecommunications; Signal-to-noise ratio (imaging)","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":[0.002180455,0.001410483,0.001257989,0.001024312,0.0005958726,0.002206744,0.001637101,0.001343646,0.004023697],"category_scores_gemma":[0.007131611,0.0006893487,0.0005368277,0.001482508,0.001632087,0.001639143,0.00161611,0.001615837,0.0005890684],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003097609,"about_ca_system_score_gemma":0.002593144,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009050893,"about_ca_topic_score_gemma":0.005623275,"domain_scores_codex":[0.9987535,0.0005509108,0.00003234481,0.000134702,0.0002458481,0.0002826059],"domain_scores_gemma":[0.9971232,0.002252626,0.0001523401,0.00007933731,0.0003008507,0.00009162741],"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":[0.00005154289,0.00003201714,0.0001642994,0.000128008,0.00003524537,0.00008202706,0.00007382495,0.9135801,0.0007500316,0.07035296,0.002436074,0.01231374],"study_design_scores_gemma":[0.000007684394,0.00001038987,0.00006073551,0.00001959653,0.000006036395,0.00001510368,0.00001993121,0.9715029,0.0001345956,0.0274751,0.0007396317,0.00000823623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02408622,0.004617029,0.9277067,0.001536878,0.0002464211,0.0001136194,0.0002377906,0.0002536239,0.04120174],"genre_scores_gemma":[0.9279649,0.004039858,0.05285165,0.0003826738,0.0002820604,0.0003799803,0.0001946691,0.0001382482,0.01376593],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009050893,"threshold_uncertainty_score":0.02247483,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03036873422748745,"score_gpt":0.2552283356179824,"score_spread":0.2248596013904949,"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."}}