{"id":"W1974877648","doi":"10.1016/j.compeleceng.2014.06.004","title":"Energy-efficient design of channel sensing in cognitive radio networks","year":2014,"lang":"en","type":"article","venue":"Computers & Electrical Engineering","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":false,"ca_institutions":"Huawei Technologies (Canada)","funders":"Natural Science Foundation of Jiangsu Province","keywords":"Cognitive radio; Additive white Gaussian noise; Rayleigh fading; Fading; Efficient energy use; Energy (signal processing); Energy consumption; Computer science; Throughput; Channel (broadcasting); Nakagami distribution; Electronic engineering; Signal-to-noise ratio (imaging); Spectral efficiency; Telecommunications; Mathematical optimization; Wireless; Engineering; Mathematics; Electrical engineering; Statistics","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":[0.0008155012,0.0008042902,0.0005927241,0.0004429851,0.0004551083,0.001381451,0.001200465,0.0007344696,0.001732028],"category_scores_gemma":[0.00291153,0.0005224133,0.0003094565,0.0003298152,0.0005734977,0.0008281983,0.0008154106,0.0006030774,0.0003229906],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000985381,"about_ca_system_score_gemma":0.001427303,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001442225,"about_ca_topic_score_gemma":0.00319248,"domain_scores_codex":[0.9991127,0.0002561467,0.00003314963,0.0001329712,0.0003222211,0.0001429321],"domain_scores_gemma":[0.9989703,0.0005714881,0.00009370164,0.00008682513,0.0002409285,0.00003683841],"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":[0.0002357267,0.0001481101,0.0004721605,0.0001653806,0.00006491207,0.00008968533,0.00009587171,0.833223,0.02782279,0.0599677,0.001603016,0.0761116],"study_design_scores_gemma":[0.00001149285,0.00003304835,0.000087353,0.000008779577,0.00001056459,0.0000363449,0.00001335283,0.9836419,0.003387383,0.01211459,0.000647718,0.000007560029],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01752287,0.0006151011,0.9742287,0.0002511168,0.00007730528,0.00005348452,0.00004216272,0.0001553971,0.007053884],"genre_scores_gemma":[0.9119701,0.0004627409,0.08445253,0.0001673723,0.00006237585,0.0001110313,0.00004057125,0.0000502838,0.002682953],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001732028,"threshold_uncertainty_score":0.007149518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009622967295693022,"score_gpt":0.1939608967018801,"score_spread":0.1843379294061871,"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."}}