{"id":"W2147600250","doi":"10.1109/tcomm.2013.072913.120881","title":"Opportunistic Spectrum Access Using Partially Overlapping Channels: Graphical Game and Uncoupled Learning","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":77,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Throughput; Computer science; Interference (communication); Potential game; Channel (broadcasting); Selection (genetic algorithm); Aggregate (composite); Convergence (economics); Heterogeneous network; Computer network; Game theory; Point (geometry); Distributed computing; Mathematical optimization; Wireless; Wireless network; Nash equilibrium; Telecommunications; Artificial intelligence; 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":[0.001149361,0.0009342898,0.000956006,0.0004932358,0.0004907937,0.001058113,0.001433249,0.00105456,0.001068892],"category_scores_gemma":[0.004116225,0.0003567352,0.0005326284,0.0005615751,0.001700204,0.001779565,0.001554508,0.001015718,0.0001219981],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001180258,"about_ca_system_score_gemma":0.001003072,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002491332,"about_ca_topic_score_gemma":0.00238857,"domain_scores_codex":[0.9986113,0.0007125086,0.00004188484,0.0001998006,0.0002395151,0.0001949615],"domain_scores_gemma":[0.9973286,0.001920693,0.0002876334,0.0001329119,0.0001516834,0.000178438],"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":[0.0001604649,0.00008110427,0.0006369655,0.00006215729,0.00005316935,0.00019197,0.0001170894,0.9350416,0.001730329,0.04552317,0.0004138924,0.01598808],"study_design_scores_gemma":[0.00001951228,0.00003619231,0.00007031984,0.000003888383,0.000007016573,0.00003200256,0.00001598608,0.985897,0.000231784,0.01348838,0.0001911169,0.000006805757],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08500165,0.0001397003,0.9091927,0.0002876351,0.00002445169,0.00008915374,0.00004406399,0.0000986603,0.005121859],"genre_scores_gemma":[0.9655748,0.0001119747,0.03236393,0.00006336761,0.00001362657,0.00009950157,0.00002169175,0.00001075086,0.001740408],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002491332,"threshold_uncertainty_score":0.008563459,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06002980338865202,"score_gpt":0.2928954113969637,"score_spread":0.2328656080083117,"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."}}