{"id":"W2897411018","doi":"10.1109/tnse.2018.2877441","title":"Learning-Theoretic Multi-Channel Spectrum Sensing and Access in Full-Duplex Cognitive Radio Networks with Unknown Primary User Activities","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Network Science and Engineering","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cognitive radio; Computer science; Optimization problem; Convex optimization; Wireless; Throughput; Channel (broadcasting); Computer network; Robust optimization; Mathematical optimization; Regular polygon; Algorithm; Mathematics; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006413223,0.0003088967,0.0002995896,0.0003769046,0.0007432827,0.0006364788,0.0002663822,0.00008327574,0.000002621084],"category_scores_gemma":[0.00001249555,0.0002859604,0.0000332109,0.001823359,0.0007022395,0.001216585,0.00002390619,0.0005701006,9.512942e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001267584,"about_ca_system_score_gemma":0.0001076867,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003892714,"about_ca_topic_score_gemma":0.0002925437,"domain_scores_codex":[0.9978369,0.0000533676,0.00021387,0.0007252143,0.000333757,0.0008369145],"domain_scores_gemma":[0.99911,0.0003296037,0.00006545204,0.0001956426,0.00009746531,0.0002018075],"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":[0.0000728633,0.00003110064,0.00007552664,0.00001687592,0.00002335243,0.00003859678,0.001047765,0.965539,0.0002795451,0.0001809983,0.000004191498,0.0326902],"study_design_scores_gemma":[0.0006329423,0.0003615268,0.002968276,0.0004360743,0.00001975725,0.0001967316,0.0000902998,0.9940752,0.0007727555,0.00002564612,0.00002742348,0.0003933783],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2930037,0.00011808,0.7060668,0.00007960637,0.0003604147,0.0001709517,4.540687e-7,0.0001291158,0.00007085589],"genre_scores_gemma":[0.9945389,0.0003481974,0.004675777,0.0001278038,0.0002403829,0.000006383382,4.402873e-7,0.00002744899,0.00003465741],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7015352,"threshold_uncertainty_score":0.9999592,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008808122102316235,"score_gpt":0.2136440833952946,"score_spread":0.2048359612929783,"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."}}