{"id":"W2055061559","doi":"10.1109/vetecf.2008.264","title":"Resource Allocation for Downlink Spectrum Sharing in Cognitive Radio Networks","year":2008,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Group for Research in Decision Analysis; Institut National de la Recherche Scientifique; University of Waterloo","funders":"","keywords":"Cognitive radio; Heuristics; Resource allocation; Computer science; Telecommunications link; Resource management (computing); Base station; Mathematical optimization; Optimization problem; Orthogonal frequency-division multiplexing; Frequency allocation; Shared resource; Computer network; Distributed computing; Telecommunications; Algorithm; Wireless; 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":[0.0008424115,0.0006127797,0.0007464482,0.0003627463,0.0004604621,0.001108585,0.000587356,0.0007183128,0.001931901],"category_scores_gemma":[0.002748017,0.0003075219,0.0003057964,0.0006162135,0.0007842821,0.0009964382,0.0008223531,0.0007371479,0.0002340338],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008730174,"about_ca_system_score_gemma":0.001216803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002854232,"about_ca_topic_score_gemma":0.001995455,"domain_scores_codex":[0.9993585,0.0003038246,0.00001456541,0.00005845561,0.0001241759,0.0001405303],"domain_scores_gemma":[0.9994536,0.0003936111,0.0000472402,0.00002807743,0.00004708341,0.00003048789],"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":[0.00008580279,0.0000633554,0.000262812,0.0000619062,0.0000167607,0.0000743123,0.00006411163,0.9314659,0.002219305,0.038308,0.001024787,0.02635295],"study_design_scores_gemma":[0.00001234439,0.00002039881,0.00004951587,0.000005937483,0.00000548056,0.00001878207,0.00001981248,0.9873496,0.0004984205,0.01153096,0.0004848869,0.000003834244],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03359285,0.0004282363,0.9572173,0.0002980558,0.00003908772,0.00005284773,0.00003075152,0.00006393124,0.008277089],"genre_scores_gemma":[0.8949772,0.0005635284,0.1013737,0.0001392332,0.00006777077,0.0001910831,0.00003797443,0.00004877595,0.002600777],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002854232,"threshold_uncertainty_score":0.006462872,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01525119653415224,"score_gpt":0.2247679878254068,"score_spread":0.2095167912912546,"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."}}