{"id":"W2160081863","doi":"10.1109/twc.2008.070073","title":"Competitive spectrum sharing in cognitive radio networks: a dynamic game approach","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":357,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Cognitive radio; Computer science; Nash equilibrium; Stochastic game; Oligopoly; Mathematical optimization; Bounded rationality; Game theory; Wireless; Frequency allocation; Backward induction; Fictitious play; Potential game; Sequential game; Computer network; Telecommunications; Mathematical economics; Mathematics; Artificial intelligence; Cournot competition","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":[0.001031228,0.0009466453,0.001028273,0.0006192989,0.0006467405,0.001900496,0.001782077,0.001551156,0.001647742],"category_scores_gemma":[0.002289562,0.000501096,0.0007608791,0.0006791202,0.00197199,0.001638158,0.001247557,0.001275933,0.0001962947],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002052917,"about_ca_system_score_gemma":0.001486588,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007643168,"about_ca_topic_score_gemma":0.005759018,"domain_scores_codex":[0.9990774,0.0004686876,0.00001807294,0.0001058721,0.0001956238,0.0001341595],"domain_scores_gemma":[0.999189,0.0005511438,0.00008126121,0.0000292291,0.00008091345,0.00006851029],"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":[0.00004093203,0.00005001638,0.0002318133,0.00005623197,0.00004684867,0.0001608932,0.0001385873,0.7301909,0.0008919968,0.2604514,0.000820477,0.006919818],"study_design_scores_gemma":[0.00001491108,0.00002067855,0.00005741687,0.00000599494,0.00000756013,0.00003004588,0.00002670069,0.9529613,0.00006923551,0.04599348,0.0008024335,0.00001019406],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03594965,0.001385871,0.9363605,0.001156006,0.0001198222,0.00009143931,0.00005729109,0.00004960373,0.02482989],"genre_scores_gemma":[0.9437606,0.001263259,0.04780828,0.0002210001,0.0001365081,0.0002105992,0.00003573239,0.00002058823,0.006543388],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007643168,"threshold_uncertainty_score":0.01519734,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03045374575210665,"score_gpt":0.2609240220357178,"score_spread":0.2304702762836111,"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."}}