{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007421786,0.0001182907,0.0001304407,0.00008410348,0.0000580157,0.00001011185,0.00007879969,0.0000819354,0.00002809869],"category_scores_gemma":[0.00002048141,0.00013382,0.00002880471,0.0002648833,0.00001980207,0.0001606829,0.00001603172,0.0001254852,0.000004517213],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009905664,"about_ca_system_score_gemma":0.000005386859,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000440491,"about_ca_topic_score_gemma":0.00004326062,"domain_scores_codex":[0.9992802,0.000006547104,0.0002005978,0.0001871746,0.0000619244,0.0002635336],"domain_scores_gemma":[0.9997055,0.0001009598,0.00002344987,0.0001098616,0.00002070659,0.00003954823],"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":[0.00001888882,0.000008149153,0.001130002,0.000007671054,0.00000878114,0.000002472441,0.0001258028,0.9955527,0.00001687584,0.0004431686,0.0005231883,0.002162369],"study_design_scores_gemma":[0.0005730542,0.00001539598,0.001623886,0.00004126082,0.000004533804,0.00000691228,0.0000393146,0.9963333,0.0004394078,0.0001624058,0.0005958749,0.0001646806],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02416569,0.0002644853,0.9698137,0.00005054281,0.00009601532,0.000383421,0.000001525142,0.0003396276,0.004884965],"genre_scores_gemma":[0.989304,0.000275946,0.00951378,0.00005104292,0.0002314435,0.00008035883,0.0001084056,0.00004548027,0.0003895088],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9651383,"threshold_uncertainty_score":0.5457023,"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."}}