{"id":"W2042671868","doi":"10.1016/j.dsp.2014.09.012","title":"Optimization of multi-level hierarchical cluster-based spectrum sensing structure in cognitive radio networks","year":2014,"lang":"en","type":"article","venue":"Digital Signal Processing","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"Imperial College London; University of Windsor","keywords":"Cognitive radio; Overhead (engineering); Computer science; Throughput; Energy (signal processing); Cluster (spacecraft); Fusion rules; Efficient energy use; Hierarchical clustering; Distributed computing; Algorithm; Computer network; Cluster analysis; Artificial intelligence; Telecommunications; Mathematics; Engineering; Wireless","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007949243,0.0007075173,0.0009130164,0.0006117675,0.0007424339,0.001002347,0.001673131,0.001027367,0.001572012],"category_scores_gemma":[0.002344457,0.0004641411,0.0004847502,0.0006566006,0.0005961677,0.0009549145,0.001216013,0.0005564134,0.0001843882],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001422351,"about_ca_system_score_gemma":0.001719246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005422066,"about_ca_topic_score_gemma":0.006102913,"domain_scores_codex":[0.9994689,0.0001585404,0.00001791535,0.000107806,0.0001233518,0.0001236308],"domain_scores_gemma":[0.9991781,0.0003636276,0.000107297,0.00004626234,0.000225183,0.00007942897],"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":[0.00006135628,0.00004978725,0.0002835658,0.0000333609,0.00001881753,0.00002463308,0.00002839098,0.9821197,0.002378787,0.003105054,0.0004170866,0.01147941],"study_design_scores_gemma":[0.000004510759,0.00002001369,0.0000820016,0.000001810123,0.000004559236,0.000006688114,0.00000962678,0.9987503,0.000227572,0.0008353717,0.00005458844,0.000002800457],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1085875,0.0003820851,0.8847445,0.0002812913,0.00006618598,0.00008805792,0.00005908446,0.000231344,0.005559923],"genre_scores_gemma":[0.9495541,0.00009966531,0.04928644,0.00004809976,0.00001695412,0.0000616384,0.00002917365,0.00002497544,0.0008788654],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005422066,"threshold_uncertainty_score":0.01078099,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01769529042172384,"score_gpt":0.239334111698421,"score_spread":0.2216388212766972,"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."}}