{"id":"W2565191940","doi":"10.1155/2016/6024928","title":"Channel Selection Policy in Multi-SU and Multi-PU Cognitive Radio Networks with Energy Harvesting for Internet of Everything","year":2016,"lang":"en","type":"article","venue":"Mobile Information Systems","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Division of Graduate Education; Natural Science Foundation of Jiangsu Province; Government of Jiangsu Province; Fundamental Research Funds for the Central Universities; Graduate Research and Innovation Projects of Jiangsu Province; National Natural Science Foundation of China","keywords":"Computer science; Cognitive radio; Underlay; Computer network; Channel (broadcasting); Network packet; Throughput; Spectrum management; Energy harvesting; Transmission (telecommunications); Energy (signal processing); Telecommunications; Wireless; Signal-to-noise ratio (imaging)","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":[0.001841276,0.0006355279,0.0008646255,0.0006826307,0.001163745,0.001184319,0.001737411,0.000860678,0.0009007346],"category_scores_gemma":[0.004183221,0.0004153621,0.0004087971,0.0006741821,0.001331448,0.00110385,0.0008675853,0.0008189065,0.0001576705],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001355311,"about_ca_system_score_gemma":0.001579982,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004553485,"about_ca_topic_score_gemma":0.00444176,"domain_scores_codex":[0.9982663,0.000593006,0.00005283665,0.000294352,0.0003956634,0.0003979003],"domain_scores_gemma":[0.9958574,0.00253175,0.0004507248,0.0001955958,0.0005732971,0.000391196],"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":[0.0008049421,0.0003641449,0.00380479,0.0001519014,0.0001687751,0.0008419952,0.0004686664,0.8706644,0.01450148,0.05124608,0.003050585,0.05393229],"study_design_scores_gemma":[0.00002631212,0.0001050122,0.0003669874,0.00000497618,0.00002270833,0.000134437,0.0000629223,0.9919776,0.001193461,0.0057052,0.000384053,0.00001636304],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2694027,0.0007939636,0.7226964,0.0006568215,0.0001477695,0.0001717724,0.00005766735,0.0002867184,0.005786201],"genre_scores_gemma":[0.9889683,0.00009119642,0.01024302,0.00007773322,0.00002245433,0.00004803281,0.00001056347,0.000007518719,0.0005312849],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004553485,"threshold_uncertainty_score":0.009833455,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01598120081336353,"score_gpt":0.2398252290824184,"score_spread":0.2238440282690549,"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."}}