{"id":"W2740470694","doi":"10.1109/icc.2017.7997274","title":"Sensing, probing, and transmitting strategy for energy harvesting cognitive radio","year":2017,"lang":"en","type":"article","venue":"","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Cognitive radio; Markov decision process; Partially observable Markov decision process; Computer science; Throughput; Markov process; Transmission (telecommunications); Power control; Channel (broadcasting); Energy harvesting; Energy (signal processing); Control channel; State space; Optimal control; Markov chain; Wireless; Markov model; Power (physics); Computer network; Telecommunications; Mathematical optimization; Machine learning; 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":[0.001168334,0.0007691489,0.0008033817,0.0003506986,0.0004376143,0.0008703475,0.00108591,0.0009939721,0.0008797806],"category_scores_gemma":[0.002382037,0.0004256057,0.0005778185,0.0004571412,0.001003853,0.0007794024,0.0006531392,0.0007218189,0.000119205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009865335,"about_ca_system_score_gemma":0.001300474,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003693176,"about_ca_topic_score_gemma":0.003100619,"domain_scores_codex":[0.9992881,0.0002411001,0.00002890095,0.0001362568,0.0001445835,0.0001609612],"domain_scores_gemma":[0.9986615,0.0008264012,0.0002408851,0.00006708896,0.0001025118,0.0001016811],"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":[0.000199946,0.0001518845,0.001129084,0.00007310531,0.00005585285,0.0004450976,0.0001495547,0.9344411,0.00800483,0.04417047,0.0004485466,0.0107304],"study_design_scores_gemma":[0.00001431738,0.00004302878,0.0001244148,0.000002221355,0.00001091298,0.00002678782,0.00001115347,0.9945683,0.0003848851,0.004746044,0.00006030629,0.000007618208],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2087902,0.0005349068,0.7838743,0.0005444009,0.00006204172,0.0001291185,0.00007572988,0.0001348428,0.005854541],"genre_scores_gemma":[0.9857975,0.0001467974,0.01281342,0.00004994901,0.00001623524,0.00005710376,0.00001644731,0.000007958755,0.001094621],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003693176,"threshold_uncertainty_score":0.007343352,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0313980790264498,"score_gpt":0.2678740181201316,"score_spread":0.2364759390936818,"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."}}